{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Output Signal to Noise Ratio\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The signal to noise ratio at the output of the receiver is expressed as (Equation 4.37)\n",
    "\n",
    "$$\n",
    "{SNR}_o = \\frac{P_t\\,G^2\\,\\lambda^2\\,\\sigma}{(4\\pi)^3\\,k\\,T_0\\,B\\,F\\,r^4}\n",
    "$$\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the target range (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_min_range = 10e3\n",
    "\n",
    "target_max_range = 100e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `linspace` routine from `scipy` for setting up the target range array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "target_range = linspace(target_min_range, target_max_range, 2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the receiver noise figure (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    " noise_figure = 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the radar losses (dB), antenna gain (dB) and the target RCS (dBsm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = 6.0\n",
    "\n",
    "antenna_gain = 25\n",
    "\n",
    "target_rcs = -10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the keyword args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = {'target_range': target_range,\n",
    "\n",
    "          'system_temperature': 300,\n",
    "\n",
    "          'bandwidth': 10e6,\n",
    "\n",
    "          'noise_factor': 10.0 ** (noise_figure / 10.0),\n",
    "\n",
    "          'losses': 10.0 ** (losses / 10.0),\n",
    "\n",
    "          'peak_power': 100e3,\n",
    "\n",
    "          'antenna_gain': 10.0 ** (antenna_gain / 10.0),\n",
    "\n",
    "          'frequency': 1e9,\n",
    "\n",
    "          'target_rcs': 10.0 ** (target_rcs / 10.0)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `output_snr` routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.radar_range import radar_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the signal to noise ratio at the output of the receiver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_snr = radar_range.output_snr(**kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `log10` routine from scipy for displaying the signal to noise ratio in dB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import log10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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COxqFdzE2ykLowOE7baLqdEo3cJRwnXR0dCQ9ApAXjcI7GoV3NArvYmyUhdCBQXVVOmjCcN0+Y4GWtcYXkRdmlvQIQF40Cu9oFN7RKLyLsVEWQieOn7yZmttX6I/TX096lGg1NDQkPQKQF43COxqFdzQK72JslIXQiQkjBmnHTQdpyiPzlMmEpMeJUoznbKO80Ci8o1F4R6PwLsZGWQgdOWHyKM17u1n/enlR0qNEqb29PekRgLxoFN7RKLyjUXgXY6MshI58dNth2nBAtaZMm5f0KAAAAADKAAuhI1XplI7aZVM98NIizVvclPQ40YnxnG2UFxqFdzQK72gU3sXYKAuhM0fvuqkqzDTlkVeTHiU6MR6iR3mhUXhHo/CORuFdjI2yEDqz0Xo1+th2w3Tr9NfU1NaZ9DhRifEvIMoLjcI7GoV3NArvYmyUhdCh4yeP0vK2Tk2dsSDpUQAAAACUMBZCh3badJC222SgpkybpxC4BEWh6uvrkx4ByItG4R2NwjsahXcxNspC6JCZ6fjJo/TyW416ePbbSY8Tjc5OTrGFbzQK72gU3tEovIuxURZCpw6aMExDGqp0zUOvJD1KNGK8ECjKC43COxqFdzQK72JslIXQqZrKCh23+yjdP2uRZr+1POlxAAAAAJQgFkLHjt51U1WnU7rmoblJjxKFGM/ZRnmhUXhHo/CORuFdjI26WwjN7Awzm25mbWZ2XbePfcjMXjSzZjO738w2S2jMfrFBQ7UOnzhCtz25QIsb4zv83N8ymUzSIwB50Si8o1F4R6PwLsZG3S2EkhZK+r6ka7veaGZDJE2VdJ6kwZKmS7q536frZyfvOVrtnRn9jgvV96ilpSXpEYC8aBTe0Si8o1F4F2Oj7hbCEMLUEMKfJHV/ec1PSHouhHBrCKFV0gWStjezsf09Y3/aYsMGfWjsRvr9f15Va8eKpMcBAAAAUELcLYR5jJc0c+U7IYQmSXNyt5e0U/baXG83tet2LlSfV11dXdIjAHnRKLyjUXhHo/AuxkbTSQ/QCw2SFnW7bamkAd0/0cxOk3SaJI0YMUKtra1qb29Xe3u7pOyTPTs7O1e9LGx9fb0ymcyqQ7wrf5HNzc2SpNraWqVSKTU1NUmSqqurlU6nV71fVVWlqqoqNTY2rnq/urpajY2NCiGosrJS1dXVam5uViaTUTqdVk1Nzar3U6mU6urq1Nraqs7OzlXvt7W1qaOjQ2MGSuOHDdBV/5qtD42uVcpMDQ0NUT8myz2Gtra2VY9hXR9T7ndfUo+pFH9P5fyYlixZoubm5pJ6TKX4eyrnx7Ry7lJ6TKX4eyrnx7R8+XI1NzeX1GMqxd9TOT+m1tZWmZm7x5SPhRDyfkJSzOz7kkaEEE7Ivf9LSZUhhNO7fM4zki4IIdy2pvuZNGlSmD59erHHLbo/zVigL978lK49YZI+OHZo0uO4tHjxYg0ZMiTpMYA1olF4R6PwjkbhnddGzeyJEMKk1X0splNGn5O0/cp3zKxe0ha520vegROGadjAGl39IJegAAAAANA33C2EZpY2sxpJFZIqzKzGzNKSbpe0rZkdnvv4+ZKeDiG8mOS8/aWyIqUTJo/StDlv69kFS5Mex6Xa2tqkRwDyolF4R6PwjkbhXYyNulsIJX1LUoukcyUdk/vzt0IIiyQdLulCSe9I2lXSkUkNmYQjd9lUDdVpXfGvOUmP4lJP50cDSaNReEej8I5G4V2MjbqbOIRwQQjBur1dkPvYP0IIY0MItSGEfUII85Kdtn8NrK3U0bttqr8+81/NW9yU9DjurHziLeAVjcI7GoV3NArvYmzU3UKI/E7eY7TSFSld+e9Xkh4FAAAAQORYCCOz0Xo1OmLiCN32xOt6a1lr0uO4Ul1dnfQIQF40Cu9oFN7RKLyLsVEWwgh9du/N1ZnJ6JqHeMXRrtLpmC6riXJEo/CORuEdjcK7GBtlIYzQZhvU68AJw/X7/7yqpc0dSY/jRoznbKO80Ci8o1F4R6PwLsZGWQgj9fkPbKGm9hX63X/mJT0KAAAAgEixEEZq3PD1tM/WG+rah+eppX1F0uO4UFVVlfQIQF40Cu9oFN7RKLyLsVEWwoidvs+WWtLUrlumv5b0KC7E+BcQ5YVG4R2NwjsahXcxNspCGLGdR62viZutr6v+/Yo6VmSSHidxjY2NSY8A5EWj8I5G4R2NwrsYG2UhjJiZ6fR9ttCCd1v0pxkLkh4HAAAAQGRYCCP3wbEbadyw9XTZ/bPVWeZHCWM8RI/yQqPwjkbhHY3CuxgbZSGMnJnprA+N0by3m3XHzIVJj5OoGC8EivJCo/CORuEdjcK7GBtlISwB+48bqrEbD9Cl983WikxIepzExHjONsoLjcI7GoV3NArvYmyUhbAEpFKmsz80Rq8sbtJfni7fo4QhlO8yjDjQKLyjUXhHo/AuxkZZCEvER8ZvrK2HDtAl/3y5bI8SVlZWJj0CkBeNwjsahXc0Cu9ibJSFsESkUqYzP7Sl5ixq0l3P/DfpcRIR4znbKC80Cu9oFN7RKLyLsVEWwhLysW2HacxGDfrVP19WpgyPEjY3Nyc9ApAXjcI7GoV3NArvYmyUhbCEZI8SjtHLbzXqb8++kfQ4/S6TKe/LbsA/GoV3NArvaBTexdgoC2GJOXC7Ydpiw3pdUoZHCdPpdNIjAHnRKLyjUXhHo/AuxkZZCEtMRcp05gfHaNaby3XPc+V1lLCmpibpEYC8aBTe0Si8o1F4F2OjLIQl6ODth2vzIfX6xT/K6xVHYzxnG+WFRuEdjcI7GoV3MTbKQliCKlKmL354K816c3lZXZcwxnO2UV5oFN7RKLyjUXgXY6MshCXqoO2GaezGA/Tze19Sx4r4wlwbqRQ5wzcahXc0Cu9oFN7F2Gh8E6MgqZTpK/tvrXlvN+u2J15Pepx+UVdXl/QIQF40Cu9oFN7RKLyLsVEWwhK23zYbafuRg3TJP19WW+eKpMcputbW1qRHAPKiUXhHo/CORuFdjI2yEJYwM9PX9t9aC5e26oZH5yc9TtF1dnYmPQKQF43COxqFdzQK72JslIWwxO2x5QbabfPBuuz+2Wpujy/Q3ojxnG2UFxqFdzQK72gU3sXYaHwTo1fMTF/7yNZa3Niu66bNS3qcoorxnG2UFxqFdzQK72gU3sXYKAthGZi42WB9cOxGuvJfr2hpS0fS4xRNW1tb0iMAedEovKNReEej8C7GRlkIy8RX9t9KS1s6dM2DryQ9StF0dJTusovSQKPwjkbhHY3CuxgbZSEsE+OHD9SB2w3T1Q/N1aLl8f2Xi0KYWdIjAHnRKLyjUXhHo/AuxkZZCMvIV/bfSm2dGV3yz5eTHqUoGhoakh4ByItG4R2NwjsahXcxNspCWEY237BBn9llpG58bL5eWdSY9Dh9LsZztlFeaBTe0Si8o1F4F2OjLIRl5uwPbaWqdEo/uWdW0qP0ufb29qRHAPKiUXhHo/CORuFdjI2yEJaZDQdU67S9N9ffnn1DT85/J+lxAAAAACSIhbAMnbrX5hrSUK0f/vUFhRCSHqfPxHjONsoLjcI7GoV3NArvYmyUhbAM1Ven9cX9xujxee/oHy+8lfQ4fSbGQ/QoLzQK72gU3tEovIuxURbCMvXpnUdq8yH1+vHdL6pzRSbpcfpEjH8BUV5oFN7RKLyjUXgXY6MshGWqsiKlcw4Yq9lvNerWJ15PehwAAAAACWAhLGMfGT9UEzdbXz+/9yU1t3cmPc46q6+vT3oEIC8ahXc0Cu9oFN7F2CgLYRkzM339o2P11vI2XfXvV5IeZ511dsa/1KK00Si8o1F4R6PwLsZGWQjL3KRRg3XghGG64l9z9N+lLUmPs05ivBAoyguNwjsahXc0Cu9ibJSFEDr3gLHKBOmiu0vvYvUAAAAA1oyFEBo5uE6n7jVat89YoBkRX6w+xnO2UV5oFN7RKLyjUXgXY6MshJAkfX6fLbXhgGp99y/PR3ux+kymNC6fgdJFo/CORuEdjcK7GBtlIYQkqaE6ra99ZGvNmP+u7pi5MOlx1kpLS9zPgUTpo1F4R6PwjkbhXYyNshBilSN2GqHxw9fTj//2olraVyQ9DgAAAIAiYyHEKqmU6fyDxmnh0lb95sH4LkNRV1eX9AhAXjQK72gU3tEovIuxURZCvMeum2+gj223sS5/YI7eWNqa9DgAAAAAioiFEO/z9Y9uoxWZoIvufjHpUXqlubk56RGAvGgU3tEovKNReBdjoyyEeJ+Rg+t08l6jNXXGAj3xaryXoQAAAACQHwshVuuMfbfUxuvV6Nt3PKsVmTguQ1FbW5v0CEBeNArvaBTe0Si8i7FRFkKsVn11Wt88cBs9u2CZbnhsftLjFCSVImf4RqPwjkbhHY3CuxgbjW5iM3vAzFrNrDH3NivpmUrVQROGafIWG+jie2ZpSVN70uP0qKmpKekRgLxoFN7RKLyjUXgXY6PRLYQ5Z4QQGnJvWyc9TKkyM33nkPFqauvUT+6J6wVmAAAAAPQs1oUQ/WTM0AE6YfIo3fT4a5r52rtJj5NXdXV10iMAedEovKNReEej8C7GRmNdCH9oZovN7GEz2yfpYUrd2fuN0ZCGap3/52eVcfwCM+l0OukRgLxoFN7RKLyjUXgXY6PxTSz9n6TnJbVLOlLSnWa2QwhhzspPMLPTJJ0mSSNGjFBra6va29vV3p59Hlx9fb06OzvV1ta26v1MJqOWlhZJUl1dnaT/XUektrZWqVRq1TnB1dXVSqfTq96vqqpSVVWVGhsbV71fXV2txsZGhRBUWVmp6upqNTc3K5PJKJ1Oq6amZtX7qVRKdXV1am1tVWdn56r329ra1NHRITNTQ0OD2traVj2GhoaGfn1MX953lL5+xyxd88ALOmzCRi4fU0tLi4YMGVLWvycek+/HtHDhQg0cOLCkHlMp/p7K+TE1NjZq4403LqnHVIq/p3J+TG+++aYaGhpK6jGV4u+pnB/T0qVLNXz4cHePKR8Lwe8Rn0KY2d2S7goh/Gp1H580aVKYPn16P09VekII+vSV/9HLby3X/V/dR4PqqpIe6X0WL16sIUOGJD0GsEY0Cu9oFN7RKLzz2qiZPRFCmLS6j8V6ymhXQZIlPUSpMzN959DxWtbaqYvu8fnCrlVV/pZUoCsahXc0Cu9oFN7F2GhUC6GZDTKzj5hZjZmlzexoSXtLuifp2crBNsPW0/G7j9KNj83XE6++k/Q47xPjX0CUFxqFdzQK72gU3sXYaFQLoaRKSd+XtEjSYklnSvp4CMHnIasS9OX9t9LG69Xom7c/o44VmaTHeY+V51UDXtEovKNReEej8C7GRqNaCEMIi0IIO4cQBoQQBoUQdgsh3Jv0XOWkoTqt7xwyXi++sVzXPDQ36XEAAAAArIOoFkL4sP/4jbX/uKH6xT9e0mtLmpMeZ5UYD9GjvNAovKNReEej8C7GRlkIsVYuOGS8Ksx0/p+flZdXqo3xQqAoLzQK72gU3tEovIuxURZCrJXhg2r15f231v2zFumvz7yR9DiS4jxnG+WFRuEdjcI7GoV3MTbKQoi1dvzum2nbTdbTBXc+p2WtHUmP4+ZIJbAmNArvaBTe0Si8i7FRFkKstXRFSj88bILebmzTT+5O/oVeKysrkx4ByItG4R2NwjsahXcxNspCiHWy3YiBOn7yKP3+0Vf15Pxkr00Y4znbKC80Cu9oFN7RKLyLsVEWQqyzr+y/tYYOqNE3pj6j9s7krk3Y3OznFU+B1aFReEej8I5G4V2MjbIQYp01VKf1/Y9vqxffWK7LH5iT2ByZTHLLKFAIGoV3NArvaBTexdgoCyH6xH7jhuqQ7Yfr0vtf1qw3licyQzqdTuT7AoWiUXhHo/CORuFdjI2yEKLPfPvgcRpQU6lzbntaKzL9/wpLNTU1/f49gd6gUXhHo/CORuFdjI2yEKLPbNBQrQsOGa+Zr72rax+a2+/fP8ZztlFeaBTe0Si8o1F4F2OjLIToUwdPGKb9thmqi/8+S3MXN/Xr947xnG2UFxqFdzQK72gU3sXYKAsh+pSZ6cLDtlVVOqX/u+1pZfrx1NFUipzhG43COxqFdzQK72JsNL6J4d7Q9Wp03oHj9NjcJfrDY/P77fvW1dX123iR7pAAACAASURBVPcC1gaNwjsahXc0Cu9ibJSFEEXxyUkjtNeYIfrRX1/Qgndb+uV7tra29sv3AdYWjcI7GoV3NArvYmyUhRBFYWb6wWHbKUj6+tRnFELxTx3t7Ows+vcA1gWNwjsahXc0Cu9ibJSFEEUzcnCdzv3oWP37pUW66fHXiv79YjxnG+WFRuEdjcI7GoV3MTYa38SIyjG7bqY9ttxA3//L83ptSXFfhjfGc7ZRXmgU3tEovKNReBdjoyyEKKpUynTREdsrZaav3DqzqK862tbWVrT7BvoCjcI7GoV3NArvYmyUhRBFt8mgWp1/cPZVR699uHgXrO/o6CjafQN9gUbhHY3COxqFdzE2ykKIfnHExBHab5uhuuieWZr91vKifA8zK8r9An2FRuEdjcI7GoV3MTbKQoh+YWb6wSe2VX1Vhb5yy0x1rsj0+fdoaGjo8/sE+hKNwjsahXc0Cu9ibJSFEP1mowE1uvCw7TTz9aW6/IE5fX7/MZ6zjfJCo/CORuEdjcK7GBtlIUS/+th2w3TI9sP1y3++rGcXLO3T+25vb+/T+wP6Go3COxqFdzQK72JslIUQ/e67h47X4PoqfeWWmWrrXJH0OAAAAEDZYiFEvxtUV6UfHz5Bs95crovvmdVn9xvjOdsoLzQK72gU3tEovIuxURZCJGLfsRvp2N02028enKuHXl7cJ/cZ4yF6lBcahXc0Cu9oFN7F2CgLIRLzjY9toy03atCXb3lKS5rW/S9PjH8BUV5oFN7RKLyjUXgXY6MshEhMbVWFfnnkDnq3uUP/d9vTCiEkPRIAAABQVlgIkajxwwfqnAO21r3Pv6mbHn9tne6rvr6+j6YCioNG4R2NwjsahXcxNspCiMSdtMdo7bnlEH33zuc1Z1HjWt9PZ2dnH04F9D0ahXc0Cu9oFN7F2CgLIRKXSpl++qntVVOZ0tk3zVB7Z2at7ifGC4GivNAovKNReEej8C7GRlkI4cLQ9Wr0o8Mn6NkFy/Sze19KehwAAACgLLAQwo2PjN9Yn9llU1357zmaNqf3l6KI8ZxtlBcahXc0Cu9oFN7F2CgLIVw576BtNHpIvb5081N6u7F3h9wzmbU71RToLzQK72gU3tEovIuxURZCuFJXldavPrOj3mnu0FdunalMpvBLUbS0tBRxMmDd0Si8o1F4R6PwLsZGWQjhzvjhA3XegdvogVmLdPVDryQ9DgAAAFCyWAjh0jG7baaPbruxLrp7lp6c/05BX1NXV1fkqYB1Q6PwjkbhHY3CuxgbZSGES2amHx0+QRsPrNGZN8zQ0uaOpEcCAAAASg4LIdwaWFupS4/aSW8ua9U5t81UCPmfT9jc3NxPkwFrh0bhHY3COxqFdzE2ykII13YYOUjnfnSs7nnuTU155NWkxwEAAABKCgsh3Dt5z9H60NiNdOFdL+jZBUvX+Hm1tbX9OBXQezQK72gU3tEovIuxURZCuGdmuviT22uDhip94YYntbx19c8nTKXIGb7RKLyjUXhHo/Auxkbjmxhlaf36Kl3ymR31+jstOuePT6/2+YRNTU0JTAYUjkbhHY3COxqFdzE2ykKIaOw8arDOPWCs/vbsG7rmoblJjwMAAABEj4UQUTllr9E6YPzG+uHfXtRjc5e852PV1dUJTQUUhkbhHY3COxqFdzE2ykKIqJiZfvLJCdp0cJ2+cMOTemt566qPpdPpBCcDekaj8I5G4R2NwrsYG2UhRHQG1FTqimMmqrG1U2fcMEOdKzKS4jxnG+WFRuEdjcI7GoV3MTbKQogobb3xAP3wE9vpsblL9JN7ZiU9DgAAABCl+I5pAjkf33ETTX91ia789yvacdNBmrxpfdIjAXlVVVUlPQKQF43COxqFdzE2yhFCRO28g8Zp+xED9dVbn9aCZZ1JjwPkFeO/JFBeaBTe0Si8i7FRFkJErTpdoV8fM1GVFaYzbnpKze0shfCrsbEx6RGAvGgU3tEovIux0YIWQjMbaWYHmdkxuX+OLPZgeWYZbGa3m1mTmb1qZkclNQt82GRQrX555I6a+3aLvraGi9YDAAAAeL81PofQzColfTb3trmk2ZKWSxogaUszmyvpCklXhRDa+2HWlS6T1C5pqKQdJN1lZjNDCM/14wxwZu+tNtRZ+4zSL+6fp/HD19Pp+2yZ9EjA+8R4GgnKC43COxqFdzE2mu9FZWZKuk/ZhfDREMKKlR8wswpJu0g6WtIMSeOLOWSX71sv6XBJ24YQGiU9ZGZ3SDpW0rn9MQP8+twHttDst9v0k3tmaZth62nfrTdKeiTgPWK8WC3KC43COxqFdzE2mu+U0X1CCGeEEKZ1XQYlKYSwIoTwSAjhDEn7FHXC99pK0ooQwktdbpupflpI4VtTU5MuOmKCxm68ns66cYbmLo7vOjAobTE+rwDlhUbhHY3CuxgbXeMRwhDCW4XcQQhhUd+N06MGSUu73bZU2dNYVzGz0ySdJkkjRoxQa2ur2tvb1d6ePbO1vr5enZ2damtrW/V+JpNRS0uLJKmurk6S1NzcLEmqra1VKpVadaHJ6upqpdPpVe9XVVWpqqpqVQBVVVWqrq5WY2OjQgiqrKxUdXW1mpublclklE6nVVNTs+r9VCqluro6tba2qrOzc9X7bW1t6ujokJmpoaFBbW1tqx5DQ0MDj6nbY2ppaVFdXZ1+fNDmOvb3z+rk6x7TLadMkjrbon1Mpfh7KufH9O677yqEUFKPqRR/T+X8mBobG1VfX19Sj6kUf0/l/JiWL1+uEEJJPaZS/D2V82NaunSp6urq3D2mfKynF+Aws+0lbSNpeghhtpldKOlgSc9KOiuEsDjvHfQhM9tR0sMhhLout31F2aOZB6/uayZNmhSmT5/eXyMiQUuXLtXAgQMlSdNmL9ax1z6mD43dSFccM1GplCU8HfDeRgGPaBTe0Si889qomT0RQpi0uo/lXRfN7HOSHpT0JUmPmdkvJe2m7IvJbCjpF308a09ekpQ2szFdbtteEi8og/ecsz15yyH65se20d+ff1O/um92glMB/xPj8wpQXmgU3tEovIux0XwvKiNJX5X0gRDCDDPbWdJ/JG0cQlhkZjernxexEEKTmU2V9F0zO0XZVxk9VNLk/pwDPjU3N6umpmbV+yfuMUrPLVymn//jJY0bvp4+PG5ogtMB728U8IZG4R2NwrsYG+3pOoQbhRBmSFII4XFJzSufMxhCeFtSXb4vLpLTJdVKekvSjZI+zyUnIEmZTOY975uZLjxsW20/YqC+eNMMzXpjeUKTAVndGwW8oVF4R6PwLsZGC7owfRcdRZmiF0IIS0IIHw8h1IcQNg0h3JD0TPAhnX7/Ae+aygpdeewk1VendfL1j2txY1sCkwFZq2sU8IRG4R2NwrsYG+1pIawzs3+vfJM0oMv7Dyp7pA5wYU2H5zceWKOrj5+kRcvb9LnfPaG2zhWr/Tyg2GI7hQTlh0bhHY3Cuxgb7WmFPbnb+9d0e//qPpwFWCf5ztmeMGKQfvapHfSFG57U16c+o59+cnuZ8cqj6F8xPq8A5YVG4R2NwrsYG827EIYQru+vQYB11dM52wdOGKY5i7bSz+59SVtu1KDT99mynyYDsmJ8XgHKC43COxqFdzE2usaF0MxOKuQOQgjX9t04wNrr6aKbknTmB7fU7LcaddHds7T5kAYdsO3G/TAZkFVIo0CSaBTe0Si8i7HRfEcIj+3yZ5O0h6Q3JL0maaSkjSU9JImFEC7U1fX8ordmpouOmKD5S5r1pZuf0oj1d9e2m/i7eChKUyGNAkmiUXhHo/AuxkbXuMKGEPZd+SbpGUlfCyGMDCFMDiGMVPYahc/016BAT1pbWwv6vJrKCl113EStX1epU6dM11vLCvs6YF0V2iiQFBqFdzQK72JstNBjmsdI+lW32y7Ve48iAonq7Ows+HM3GlCjq4/fWUtbOnTqlOlqaeeVR1F8vWkUSAKNwjsahXcxNlroQviGpEO63XawsheHB1zo7Tnb44avp18euaOeXrBUX7x5hlZkQpEmA7JifF4ByguNwjsahXcxNlroxGdJus7MppnZzWb2iKTrJZ1ZvNGA3lmbc7Y/PG6ozjtwnO557k1deNcLRZgK+J8Yn1eA8kKj8I5G4V2MjfZ0HUJJUgjhXjPbXNLHJA2XdJeku0IIbxdzOKA32tra1uq6LyftOVqvvdOsax+eqxHr1+qkPUcXYTpg7RsF+guNwjsahXcxNlrQQihJueXvd0WcBVgnHR0da/213zpwnBa+26Lv3fW8hg+q5XIUKIp1aRToDzQK72gU3sXY6BpPGTWzqWa2c74vNrOdzWxq348F9J6ZrfXXVqRMv/j0jtp+xCCdfdMMzZj/Th9OBmStS6NAf6BReEej8C7GRvMdIbxC0q/NbD1J/5I0S9JySQMkbSVpH0nvSvpWkWcECtLQ0LBOX19bVaGrj5+kT/x6mk65frqmnj5Zm21Q30fTAeveKFBsNArvaBTexdhovusQ/j2EsLOko5W9GP2uko6QtIuk+ZKODCHsGkK4t18mBXrQ1ta2zvcxpKFa1524s1aEoBN++7jeaWrvg8mArL5oFCgmGoV3NArvYmy0x+cQhhCmS5reD7MA66S9vW+Wt803bNDVx03SUVc/qlOnTNfvT9lVNZUVfXLfKG991ShQLDQK72gU3sXYaHwXygD6waRRg/XzT+2g6a++oy/f8hTXKAQAAEBJYiFEyejrc7YPnDBM3zpwG/31mTf0nTufUwgshVg3MT6vAOWFRuEdjcK7GBtlIUTJKMYh+lP22lyn7b25pjzyqi69b3af3z/KS4ynkaC80Ci8o1F4F2OjBV+HEPCuWH8Bzz1grBYvb9NP731JQwZU6zO7bFqU74PSF+O/JFBeaBTe0Si8i7HRghdCM9tX0rGSNpG0QNLvQwj3FWswwItUyvTjIyZoSXO7vnn7M1q/rooL1wMAAKAkFHTKqJmdIulmSW9Imirpv5JuMLNTizgb0Cv19cW7ZmBlRUq/PnonTRgxSGfdNEOPvvJ20b4XSlcxGwX6Ao3COxqFdzE2WuhzCM+R9OEQwjdCCFeGEL4paf/c7YALnZ2dRb3/uqq0fnvCzhq5fq1OmTJdL/x3WVG/H0pPsRsF1hWNwjsahXcxNlroQriBpOe73TZL0uC+HQdYe/1xIdD166s05eRdVV+V1nHXPqbXljQX/XuidMR4sVqUFxqFdzQK72JstNCF8CFJPzOzOkkys3pJP5E0rViDAV5tMqhWU07eRe2dGR137WNa3BjfX3wAAABAKnwh/JykCZKWmtmbkt6VtL2kzxZrMKC3+vOc7a2GDtC1J0zSf5e26PhrH9PSlo5++96IV4zPK0B5oVF4R6PwLsZGC1oIQwj/DSF8QNLmkg6WNDqE8IEQwsKiTgf0QiaT6dfvN3GzwbrimIl66c3lOum6x9XcHt854+hf/d0o0Fs0Cu9oFN7F2OgaF0Izsy5/TplZStnLTUyXtLDLbYALLS0t/f4999l6I/3yyB01Y/47Om3KE2rtWNHvMyAeSTQK9AaNwjsahXcxNppvoVva5c+dkjq6va28DShrH9tumH58+AQ9NHuxzrpxhjpXxPdfhgAAAFCe8l2YfnyXP48u9iDAuqqrq0vse39y0kg1tXXqgjuf19f++LR++sntlUpZz1+IspJko0AhaBTe0Si8i7HRNS6EIYTXurz7yRDCxd0/x8y+LOlnxRgMiM0Je4xWU/sK/eSeWaqvrtD3Dt1WXc68BgAAANwp9DmA56/h9m/11SDAumpuTv6agKfvs4U++4HN9fv/zNeP7n5RIYSkR4IjHhoF8qFReEej8C7GRvOdMioz+2DujxVmtq+kroc7Npe0vFiDATEyM517wFg1tnbqyn+9ovVqKvWFfbdMeiwAAABgtfIuhJKuyf2zRtK1XW4Pkt6QdGYxhgLWRm1tbdIjSMouhd87dFs1504fra2s0El78jRc+GkUWBMahXc0Cu9ibDTvQhhCGC1JZjYlhHBc/4wErJ1Uys9VUFIp00+OmKCW9hX67l+eV7rCdNzuo5IeCwnz1CiwOjQK72gU3sXYaKEXpmcZhHtNTU1Jj/Ae6YqULvnMjtpvm6E6/8/P6YZH5yc9EhLmrVGgOxqFdzQK72JstKCF0MzWM7OfmdkTZvaqmc1f+VbsAYGYVaVTuuzoHbXv1hvqG7c/o1sef63nLwIAAAD6SaHHNH8taSdJ35U0WNnnDs6X9PMizQX0WnV1ddIjrFZ1ukKXHzNRe40Zov+b+rRue+L1pEdCQrw2CqxEo/CORuFdjI0WuhDuL+nwEMKfJa3I/fPTko4t2mRAL6XTPb1GUnJqKiv0m+MmafIWG+hrf5ypPz+1IOmRkADPjQISjcI/GoV3MTZa6EKYkrQ09+dGMxsk6b+SeD19uOH9nO2aygpdfdzO2mX0YH3p5qf0l6cXJj0S+pn3RgEahXc0Cu9ibLTQhXCmpA/k/vygpMskXS7ppWIMBZSq2qoKXXP8zpq42fo6+6andPez/016JAAAAJSxQhfCUyXNy/35LEktkgZJ4tVH4UZVVVXSIxSkvjqt3564i7YfMVBn3DBD9zz3RtIjoZ/E0ijKF43COxqFdzE2WuhlJ14JIczJ/XlRCOGUEMKnC/16oD/E9BewoTqt60/aRduNGKgv/OFJ/fUZjhSWg5gaRXmiUXhHo/AuxkZ7XOhyl5yYaGZDuty2vZlNlfR4UacDeqGxsTHpEXplQE2lppy0i3YYOUhn3jhDd8zkOYWlLrZGUX5oFN7RKLyLsdG8C6GZHShpgbKL3+tmdoSZXSLpAUlzxIvKAOtkQE2lrj9pF03cbH198aYZun0Gl6QAAABA/+npCOH3JX1FUn3un9dL2lDSFiGEr4UQeO18uBHjIXop+5zC607cWbttvoG+fMtM3TKdi9eXqlgbRfmgUXhHo/AuxkZ7WghHhxCuCiG0SLpCUqWkk0MIS4o/GtA7MV4IdKW6qrSuOX5n7bnlEJ3zx6d142Pzkx4JRRBzoygPNArvaBTexdhoTwvhqo+HEFZIagwhNBd3JGDtxHjOdle1VdmL1++79Yb6+tRn9LtH5iU9EvpY7I2i9NEovKNReBdjo+kePl5nZv/u8v6Abu8rhLB3348F9F4IIekR1llNZYWuOHaivvCHGTrvz8+pMxN04h6jkx4LfaQUGkVpo1F4R6PwLsZGe1oIT+72/jXFGgRYV5WVlUmP0Ceq0xX69dE76awbZ+g7dz6vjhUZnbb3FkmPhT5QKo2idNEovKNReBdjo3kXwhDC9f01CLCuYjxne02q0in96qgd9cWbn9IP/vqiGttW6Ev7jZGZJT0a1kEpNYrSRKPwjkbhXYyN9nSEEIhGc3Ozampqkh6jz1RWpHTJkTuqrrJCl/zzZTW2duq8g7ZhKYxYqTWK0kOj8I5G4V2MjbIQomRkMpmkR+hzFSnTjw+foIaatK59eK4a2zr0w09MUEWKpTBGpdgoSguNwjsahXcxNhrNQmhmD0jaTVJn7qYFIYStk5sI3qTT0eTcK6mU6fyDxmlATaUu+efLampboZ9/egdVpXt6kWB4U6qNonTQKLyjUXgXY6OxTXxGCOHqpIeAT7Ednu8NM9OXP7yVGqor9IO/vqim9k5dccxE1VRWJD0aeqGUG0VpoFF4R6PwLsZGCzrEYGbVZnahmb1iZktzt+1vZmcUdzygcM3NpX+JzNP23kI/OGw7/eulRTr+2se0vLUj6ZHQC+XQKOJGo/CORuFdjI0Wes7ZzyVtK+loSSsvrvGcpM8XY6g8fmhmi83sYTPbp5+/N5yL8ZzttXHUrpvqF5/eQU+8+o6OvvpRvdPUnvRIKFC5NIp40Si8o1F4F2OjhZ4yepikLUMITWaWkaQQwgIz26R4o73P/0l6XlK7pCMl3WlmO4QQ5nT/RDM7TdJpkjRixAi1traqvb1d7e3Z/+NcX1+vzs5OtbW1rXo/k8mopaVFklRXVyfpfxt+bW2tUqmUmpqaJGVfTjadTq96v6qqSlVVVWpsbFz1fnV1tRobGxVCUGVlpaqrq9Xc3KxMJqN0Oq2amppV76dSKdXV1am1tVWdnZ2r3m9ra1NHR4fMTA0NDWpra1v1GBoaGnhM3R5TW1ubWlpaSuoxren3tMeIal106Bide8fL+tQV0/Srw7fSkIaqqB9TKf6euj+mZcuWldxjKsXfUzk/pqamJjU0NJTUYyrF31M5P6bm5mYtXry4pB5TKf6eyvkxLVu2zOVjysdCCHk/QZLM7FVJE0IIS81sSQhhsJltKOk/IYR1vmJ27gVjPrCGDz8cQthzNV9zt6S7Qgi/ynffkyZNCtOnT1/XERGB1tbWKM/bXhfT5izWKddP1wYNVZpy0q4aPaQ+6ZGQRzk2irjQKLyjUXjntVEzeyKEMGl1Hyv0lNFbJV1vZqNzdzhM0qWSbuqLAUMI+4QQbA1v71sGV36ZJF57H6u0trYmPUK/m7zFEN1w6m5qaluhIy6fpmdeX5r0SMijHBtFXGgU3tEovIux0UIXwm9ImifpGUmDJL0saaGk7xRnrPcys0Fm9hEzqzGztJkdLWlvSff0x/dHHDo7O3v+pBK0w8hBuvVzu6umskJHXvWIHnp5cdIjYQ3KtVHEg0bhHY3CuxgbLWghDCG0hxC+GEJokDRU0oAQwpdCCP31ahaVkr4vaZGkxZLOlPTxEMKsfvr+iEBP50eXsi02bNDU0ydrxPp1OvG6x/SXpxcmPRJWo5wbRRxoFN7RKLyLsdFCLzsxzsyG5t5tkXSBmZ1vZnXFG+1/QgiLQgg7hxAGhBAGhRB2CyHc2x/fG/FY+QTdcjV0vRrd8tndtcPIQTrzxhm6ftq8pEdCN+XeKPyjUXhHo/AuxkYLXWFvUPZUUUm6WNnTNXeXdGUxhgLWxspXcipnA+sq9buTd9WHxg7Vt+94Tj/9+ywV8sJR6B80Cu9oFN7RKLyLsdFCLzsxKoQwy8xM2UtQjFf2SOHcok0G9FJHBxdpl6SaygpdccxO+ubtz+pX983W4sY2fe/QbZWuiO8UhlJDo/CORuEdjcK7GBstdCFsM7MBksZJei2EsNjM0pL8vaYqylb2v1dAktIVKf3o8O00ZECVLrt/jt5ubNcln9lRNZUVSY9W1mgU3tEovKNReBdjo705ZfQ+SddLui53207iCCEcaWhoSHoEV8xMX/vIWH374HG694U3dczVj+qdpv56HSisDo3COxqFdzQK72JstNBXGf2SpG9K+nwI4dLczRlJXyrWYEBvxXjOdn84cY/RuvQzO+npBUt1+OXT9OrbTUmPVLZoFN7RKLyjUXgXY6MFP6kohPD3EML9Xd6fHkK4rzhjAb3X3s7RrzU5cMIw/eGUXbWkuV2f+PU0PfXau0mPVJZoFN7RKLyjUXgXY6NrXAjN7O4uf37QzP69urf+GRPAutp51GDd9vnJqqvOXsD+3uffTHokAAAAJCzfi8pM6fLnq4s9CLCuYjxnu79tsWGDbj99D5183eP67O+m64JDxuu43UclPVbZoFF4R6PwjkbhXYyNrnEhDCHc0OXP1/fPOMDaa29vV00NL3zbkyEN1brxtN101o1P6fw/P6fX32nRuQeMVSoV36tixYZG4R2NwjsahXcxNlrwcwjN7EQzu8/MZuX+eWIxBwN6K8ZztpNSV5XWlcdO1HG7b6ar/v2Kzrxphlo7ViQ9VsmjUXhHo/CORuFdjI0WdB1CM/umpOMk/VTSq5I2k3SOmQ0PIVxYxPkAFElFyvSdQ8Zr5Pp1uvCvL+itZa266thJWr++KunRAAAA0E8KPUJ4iqT9QwhXhRDuCSFcJekASacVbzSgd+rr65MeITpmplP33lyXHrWjZr6+VIf9+mHNWdSY9Fgli0bhHY3COxqFdzE2WuhCWC9pUbfb3pZU27fjAGuvs7Mz6RGiddCE4brx1F21vLVTh132sB6evTjpkUoSjcI7GoV3NArvYmy00IXwbkl/MLOtzazWzMZKul7SPcUbDeidGC8E6snEzQbrT1/YQxsPrNHx1z6mGx6dn/RIJYdG4R2NwjsahXcxNlroQniGpOWSZkpqlPSUpCZJZxZpLgAJGDm4Trd9frL2HDNE37j9GX3vL89rRSYkPRYAAACKpKCFMISwLIRwnKQ6ScMk1YUQjgshvFvU6YBeiPGcbY8G1FTq6uMm6YTJo3TNQ3N12pTpamyL7/QHj2gU3tEovKNReBdjo3kXQjPbtOubpBGSaiSN6HIb4EImk0l6hJKRrkjpgkPG63uHjtcDLy3SEZdP0+vvNCc9VvRoFN7RKLyjUXgXY6M9HSGcJ2lu7p9d3+bm3l4p1mBAb7W0tCQ9Qsk5dvdR+u0JO2vBOy36+GXTNGP+O0mPFDUahXc0Cu9oFN7F2GhPC2GlpKrcP1e+DZB0rqQl4kVlgJK391Ybaurpk1VXVaFPX/Uf3TFzYdIjAQAAoI/kXQhDCCtWvuVuOkXSC5I+KunQEMKBxR4QKFRdXV3SI5SsMUMH6E9f2EM7jBiks26coZ/c86IyvNhMr9EovKNReEej8C7GRnt8URnLOk7SS5JOlHRSCOGDIYRpRZ8OgBuD66v0u1N20ZE7j9Rl98/RqVOma1lrR9JjAQAAYB309KIyh0t6VtKXJJ0dQtgthPCPfpkM6KXmZl70pNiq0xX64Se203dzLzZz2GUP65VFjUmPFQ0ahXc0Cu9oFN7F2GhPRwhvlbSRpOckfcrMpnR/K/6IADwxMx23+yj9/uRdtaSpXYde9rAemPVW0mMBAABgLfS0EH5X0qWSXpY0Zw1vgAu1tbVJj1BWdt9iA91xxp7aZFCtTrrucV317zkKgecV5kOj8I5G4R2NwrsYG03n+2AI4YJ+mgNYZ6lUj0+JRR8bObhOU0+frK/eOlM/+OuLen7hMv3o8Amq7JY92gAAIABJREFUqaxIejSXaBTe0Si8o1F4F2Oj8U0MrEFTU1PSI5Sluqq0LjtqJ311/630p6cW6pNXPKL/Lo3vGjz9gUbhHY3COxqFdzE2ykIIYJ2Zmc744Bj95rhJmru4SQf/6mE9Pm9J0mMBAACgByyEKBnV1dVJj1D2PjxuqG4/fbIaqiv0mav+o98+PJfnFXZBo/CORuEdjcK7GBtlIUTJSKfzPiUW/WTM0AH68xl7ap+tN9R37nxeX7z5KTW3dyY9lgs0Cu9oFN7RKLyLsdGCF0IzO9HM7jOzWbl/nljMwYDeivGc7VI1sLZSVx07SV/dfyvdMXOhPvHraZq3mN8PjcI7GoV3NArvYmy0oIXQzL4p6VxJN0k6K/fPc3K3A8D7pFLZ5xVed+IuemNZqw6+9CH984U3kx4LAAAAXRR6hPAUSfuHEK4KIdwTQrhK0gGSTiveaEDvVFVVJT0CVuMDW22oO8/YU5sOrtPJ10/Xz/4+Sysy5fm8QhqFdzQK72gU3sXYaKELYb2kRd1ue1tSfFdeRMmK8S9guRg5uE63fX6yPjlxhC65b7ZOuu5xvdvcnvRY/Y5G4R2NwjsahXcxNlroQni3pD+Y2dZmVmtmYyVdL+me4o0G9E5jY2PSIyCPmsoKXXTEBF142LaaNmexDr70IT27YGnSY/UrGoV3NArvaBTexdhooQvhGZKWS5opqVHSU5KaJJ1ZpLkAlCAz09G7bqZbPru7OlcEHX75NN0y/bWkxwIAAChbBS2EIYRlIYTjJNVJGiapLoRwXAjh3aJOB/RCjIfoy9WOm66vO8/cU5NGra9z/vi0vnLLzLK4NAWNwjsahXc0Cu9ibLTQVxldIkkhhEwI4a0QQiZ3+1vFHA7ojRgvBFrOhjRUa8pJu+rsD43R1Bmv6+OXPazZby1PeqyiolF4R6PwjkbhXYyNFnrKaGX3G8ysUlJF344DrL0Yz9kudxUp05c+vJWmnLSL3m5s1yGXPqw/zViQ9FhFQ6PwjkbhHY3CuxgbTef7oJk9KClIqjGzf3f78AhJ04o1GNBbIZTnpQxKwV5jNtRfz95LZ94wQ1+8+Sk9OneJvn3wONVUltZ/c6JReEej8I5G4V2MjeZdCCVdLckk7Szpmi63B0lvSrqvSHMBvVZZ+b4D2YjI0PVqdMOpu+qn976kyx+Yo5mvvatfH72TRg2pT3q0PkOj8I5G4R2NwrsYG7VCtlgzGxtCeLEf5ulzkyZNCtOnT096DPSD1tZW1dTUJD0G+sD9L76lL93ylDpXBP348Ak6cMKwpEfqEzQK72gU3tEovPPaqJk9EUKYtLqPFfoqo1Eugygvzc3NSY+APrLv2I1011l7aczQBn3hhif17T8/q7bOFUmPtc5oFN7RKLyjUXgXY6OFvqgM4F4mk0l6BPShTQbV6ubTdtcpe47W9Y+8qiMuf0RzFzclPdY6oVF4R6PwjkbhXYyNshCiZKTTPT0lFrGpSqf0rYPG6apjJ2r+kmYddMmDmvrk60mPtdZoFN7RKLyjUXgXY6MshCgZHs/XRt/Yf/zG+tvZe2n88IH68i0z9eWbn1JjW3wXsqdReEej8I5G4V2Mja5xhTWz7xZyByGE8/tuHGDtNTc3R/mXEIUZPqhWN5y6qy69f7Yu+efLenL+O/rVZ3bSdiMGJj1awWgU3tEovKNReBdjo/mOEI4s8A1wIcZzttE76YqUvrjfVrrx1N3U1pnRJy5/WFc/+IoymTiu+UOj8I5G8f/s3Xd81eXd//HXdXKSk0mATEJYYW8VlD3CEhX3qNWqrb+qbW3rXa161/b29q5V7y7tXrbW2703TlSGgCMge+89AhjITjjX748MKSUhQJLv9T3n/Xw8eMjJyfh8m1dOufJdrlOj4jo/NtrgHkJr7TdacxCRUxUI6AjoaDEsL403vz+GO15cws+mr+SjdYX86vLBpCeHvB6tUWpUXKdGxXVqVFznx0ZPaGJjTIoxppsxJq/uT0sNJnKiEhMTvR5BWlG7pDj+ds0Q7r2wP/PW7+Oc387ho7WFXo/VKDUqrlOj4jo1Kq7zY6NNWhAaY/oZYz4HioB1tX/W1v4RcUJ5ebnXI0grM8ZwzYiuvHrzKFITYrnmkU/4+durqDrs5uEaalRcp0bFdWpUXOfHRpu6h/BPwIdAe+Ag0A74K3BdC80lcsKqq/131UlpHn07tOG1747iyjM78eeZ67n0z/PYsLfY67H+jRoV16lRcZ0aFdf5sdGmLggHA3daa78AjLW2CLgduLfFJhM5QX48ZluaT2JckAcuGcSfrz6DLftLOe93H/HkJ5ux1p0LzqhRcZ0aFdepUXGdHxtt6sTlQGzt3wuNMZ1rPzatuQYxxnzXGFNgjKkwxjx6jOcnGmNWGWNKjTEfGmO6NNfXlsjgx2O2pfmdM7ADb98ylqFd2/Hjl5dxw2MFFBZXeD0WoEbFfWpUXKdGxXV+bLSpC8I5wBW1f38BeAuYBXzQjLPsAH4GPHL0E8aYdOAl4L+oOWy1AHi2Gb+2RICKCjf+0S/ey06N5/++cRZ3T+vH7LWFTP3NbD5YtdvrsdSoOE+NiuvUqLjOj402eNuJI1lrrzji4V3AciAZeKy5BrHWvgRgjBkK5B719CXAcmvt87Xvcw81eyr7WGtXNdcM4m9VVVVejyAOCQQM14/uxqge6dzyzOdc/2gBXxvemR+f24+EuBhPZlKj4jo1Kq5To+I6PzZ6wge5WmvD1trHrbV/ttaWtMRQx9AfWHzEDCXA+tq3iwA1V5wUOVrv7BRe/e4obhybx5OfbOG8389h6bYiT2ZRo+I6NSquU6PiOj822qQ9hMaY9sAPgdOo2TNYz1o7tgXmOloysPeotxUBKcd6Z2PMjcCNALm5uZSXl1NZWUllZSUASUlJVFdX1+/STUpKIhwOU1ZWBnx57G9paSkACQkJBAIBSkpq1r+hUIhgMFj/OC4ujri4OIqLi+sfh0IhiouLsdYSGxtLKBSitLSUcDhMMBgkPj6+/nEgECAxMZHy8nKqq6vrH1dUVFBVVYUxhuTkZCoqKuq3ITk5Wdt01DbFxMRQVlYWUdsUid8nr7bp5lE5nJ4dxz1vbeDiP83lO2O7cPUZmcQETKttU0VFBYWFhfo+aZuc3aZwOEx5eXlEbVMkfp+ieZvC4TCFhYURtU2R+H2K5m2qrKykrKzMuW1qjGnKFfiMMW8DIeA5oPTI56y1/9eEj58JjGvg6bnW2tFHvO/PgFxr7dePeNtvgVhr7XeOeNtS4B5r7YuNfe2hQ4fagoKC440oEeDgwYO0adPG6zHEcUWlVfz4laW8sWQnZ3Ztx68uH0yXtKRW+dpqVFynRsV1alRc52qjxpgF1tqhx3quSXsIgZFAhrX2pM6StNaOP5mPO8JyjrjnoTEmCehe+3YRgPrf0og0JjUxlt9/9XQm9s3k7leXc85v53DXuX25eljnFj/MQ42K69SouE6Niuv82GhTzyFcwr9f6KVZGWOCxph4IAaIMcbEG2PqFqwvAwOMMZfWvs/dwBJdUEZEToYxhotPz+XdH4xlSJd2/OSVZVz7yKfs+KLM69FEREREWlVTF4QfAG8bY+4yxlx/5J9mnOUnQBnwn8DXav/+EwBr7V7gUuA+4AAwDLiyGb+2RIDk5OTjv5PIETqkJvDY9Wdx38UDWLD5AGf/ZjYvLtjWYjezV6PiOjUqrlOj4jo/NtrUcwg/bOApa62d0LwjNS+dQxg9XD1mW/xhy75Sfvj8Yj7dtJ/J/bK4/+KBZKSEmvVrqFFxnRoV16lRcZ2rjZ7yOYTW2vzmHUmk+fnxmG1xR+e0RJ65cTiPzN3IL95ZzZSHZnHfxQM5d2CHZvsaalRcp0bFdWpUXOfHRpt0yKgxJnCsPy09nIhIawoEDN8ck8eb3x9N5/aJfOfJhXz/6c/5otR/L+4iIiIiTdHURV01UHX0H2NMhTFmozHm18YY/x0wKxElKal1bh0gka9HZgovfnskt03uxZtLdzL5odl8sGr3KX9eNSquU6PiOjUqrvNjo01dEH6PmgvLTAH6AmcD7wN3AN+m5rYUv2mJAUWaqrq62usRJIIEYwJ8b2JPXv3uKNKS4rj+0QJufW7RKe0tVKPiOjUqrlOj4jo/NtrUBeGtwGXW2vettWustTOAK4DvW2vfBi4DprXUkCJNUVFxUrfJFGlU/5xUXv3uKL4/sSevLdrBpAdn8/ayXSf1udSouE6NiuvUqLjOj402dUHYBkg86m2JQGrt33cBCc01lIiIS0LBGG6d3ItXvzuKrDYhvvXEAm5+aiGFxf570RcRERE5UlMXhI8B7xljbjDGTDXGfBN4B/i/2uenAKtbYkCRpvLjMdviL/1zUnnl5lHcfnZv3lu+m8kPzuLVRdubfN9CNSquU6PiOjUqrvNjo0267QRwO7CWmpvB5wA7gT8CD9c+/yEws7mHEzkR4XDY6xEkCsTGBLg5vwdT+mVx+wtLuOWZRby+eCf3XTyArDbxjX6sGhXXqVFxnRoV1/mx0SbtIbTWhq21f7HWTrTW9rXWTqh9fLj2+XJrbVnLjirSuLIyJSitp2dWzZVIf3JeX+as3cukB2fxXMHWRvcWqlFxnRoV16lRcZ0fG21wD6Ex5hpr7eO1f7++ofez1j7SEoOJiLgupva+hRP7ZnHni0u444UlvL54Bw9cMpDcdkefdi0iIiLiHtPQb7ONMW9aa8+t/fuHDXy8tdZOaKnhmsPQoUNtQUGB12NIKygtLSUxUf8IF2+Ew5YnP9nMA2+twgB3TO3D14Z3ISZg6t9HjYrr1Ki4To2K61xt1BizwFo79FjPNbiHsG4xWPv3/JYYTEQkUgQChmtGdCW/TyY/emkp//3acl5ZtJ0HLhlIn+w2Xo8nIiIickxNvcpoPWPMZGPMD4wxI1piIJGTVVpa6vUIIuS2S+Sx68/iN185jc37Spn2u4/41TurKa86rEbFeWpUXKdGxXV+bLTRBaEx5unaW0zUPb4TeAO4CphhjLmmhecTEfEdYwwXnd6R928dx0Wnd+QPH67jnN/OoWBLkdejiYiIiPyL4+0hHAW8BmCMCQA/BK6y1p4JXFb7WMQJCQkJXo8g8i/aJcXxq8sH8+Q3hxG2lm89t4rbn1/MgZJKr0cTOSa9jorr1Ki4zo+NHm9B2NZau6f276cD8cArtY/fBrq01GAiJyoQOOEjoEVaxage6bzzH2O5YVQXXvp8O5NO8Ib2Iq1Fr6PiOjUqrvNjo8ebuNAY07X27/nA/Lp7DwJJwOFjfZCIF0pKSrweQaRB8bEx3DQimze+N5rc9onc8swivv7Pz9i633/nGkjk0uuouE6Niuv82OjxFoR/B6YbYx4E/hP45xHPjQVWttRgIiKRqG+HNrz07ZHcc34/CjbtZ8pDs3l49gaqD4e9Hk1ERESiUKMLQmvt/cAvgFjgFmvt00c8nQH8ugVnEzkhoVDI6xFEGlXXaEzA8PVR3Xjv1nGM7J7GfW+u5Pw/zGXB5gMeTyjRTq+j4jo1Kq7zY6MN3pg+UujG9NGjrKzMlyfySvQ4VqPWWt5Zvov/eX0FO4vKufLMTtw5tQ/tkuI8mlKimV5HxXVqVFznaqON3Zjef2c9ijTAj8dsS3Q5VqPGGKYO6MCMW8dxw5huPL9gGxMfnMVzBVsJhyP7F3biHr2OiuvUqLjOj41qQSgi4oCkUJAfn9eP6d8fTV56Ene8sIQr/jqfVbsOej2aiIiIRDAtCCVixMXpEDtxW1Ma7ZPdhuduGsEvLhvE+r3FnPe7j7j/zZWUVFS3woQS7fQ6Kq5To+I6PzbapAWhMeaYN6A3xtzavOOInDw//gBKdGlqo4GA4YqhnfjgtvFcPiSXv83ewKQHZ/H2sp26d6G0KL2OiuvUqLjOj402dQ/h3Q28/SfNNYjIqSouLvZ6BJFGnWij7ZLi+N9LB/Hit0eQmhDLt55YyPWPfsaWfbp3obQMvY6K69SouM6PjQYbe9IYM6H2rzHGmHzAHPF0HnCopQYTEZEaQ7q0543vjebReZt46L01TH5oFt8Z34ObxuURHxvj9XgiIiLiY40uCIF/1P43HnjkiLdbYBfwvZYYSuRk+HEXvUSXU2k0GBPgm2PymDYoh3unr+ChGWt4fsFW7p7Wj8n9sjDGHP+TiByHXkfFdWpUXOfHRo93Y/pu1tpuwJN1f6/9k2etHWmtfa2V5hQ5Lj/eCFSiS3M0mp0azx+vOoOnbhhGYlwMNz6+gOv++Rnr9/rvEBVxj15HxXVqVFznx0abdA6htfbalh5E5FT58ZhtiS7N2ejI7ulM//4Y7p7Wj883H2Dqb2bzwFsrKdbVSOUU6HVUXKdGxXV+bPR4h4wCYIzZSs1hov/GWtu5WScSOUm6+qK4rrkbjY0JcP3obpw/OIdfvL2Kv87awMsLt3PXuX258LQcHUYqJ0yvo+I6NSqu82OjTVoQAl876nEH4BbgmeYdR+TkxcbGej2CSKNaqtGMlBC/vHwwVw3rzH+/tpz/eHYRT32yhXsu6E+/nDYt8jUlMul1VFynRsV1fmzUnOwq1hiTDbxtrT2teUdqXkOHDrUFBQVejyGtoLy8nPj4eK/HEGlQazQaDlueK9jKL95ZzRellVw9rAu3TelF20T/neQurU+vo+I6NSquc7VRY8wCa+3QYz3X1PsQHksF0O0UPl6kWZWW6t5s4rbWaDQQMFx5Vmc+vG081wzvwpOfbCb/VzN56pMtHA777zAWaV16HRXXqVFxnR8bbeo5hD896k2JwLnAW80+kchJCofDXo8g0qjWbDQ1MZb/uXAAXzmzM/e8tpy7Xl7KY/M3cff5/RjZPb3V5hB/0euouE6Niuv82GhT9xB2OupPPPAgcF0LzSVywoLBpp4SK+INLxrtl9OGZ28azh+vOoND5dVc9fAn3PR4AZv3lbT6LOI+vY6K69SouM6PjZ70OYR+oXMIo4erx2yL1PG60fKqw/zjo4388cN1VB+2fGNUV26e0IM28f47AV5ahteNihyPGhXXudpos5xDaIyZYIx52Bgzvfa/E5tvRJFT58djtiW6eN1ofGwMN+f3YOYPx3PBaTn8dfYG8n+p8wvlS143KnI8alRc58dGm7QgNMbcSs0tJvYD04F9wFPGmNtacDaRE+LHY7YlurjSaGabeH51+WBe/+5o8jKSuOvlpZz3uznMW1/o9WjiMVcaFWmIGhXX+bHRpu4hvA2YYK2901r7J2vtfwITat8u4oRA4FQumivS8lxrdGBuKs/dNOJfzi+88bECNhXq/MJo5VqjIkdTo+I6PzZ6IhOvO+rxBkDHGIkzEhMTvR5BpFEuNmqM4bxBHXj/tnHcfnZvPlpXyOSHZvHAmys5WF7l9XjSylxsVORIalRc58dGm7ogvAf4hzGmpzEmwRjTC/gb8N/GmEDdnxabUqQJysvLvR5BpFEuN3rk+YUXndaRv83ZwPhfzuSx+ZuoOuy/w1/k5LjcqAioUXGfHxtt6iLur8BXgdVAMbAKuJqaRWEVUF37XxHPVFdXez2CSKP80Ghmm3h+eflgXrt5NL2ykrn71eWc/dBs3lm+i0i/KrX4o1GJbmpUXOfHRpt6o4xuLTqFSDPw4zHbEl381OjA3FSevmE476/cwwNvreSmxxdwZtd23HVuX07v3M7r8aSF+KlRiU5qVFznx0abOvHl1trNR/8BLj3qsYhn/HjMtkQXvzVqjGFSvyze+Y+x3HfxADYWlnLxn+bx3acWsmWf/y6rLcfnt0Yl+qhRcZ0fG23qgvDuBt7+k+YaRORUVVRUeD2CSKP82mgwJsDVw7ow8/bxfH9CD95fuYeJD87k3jdW8EVppdfjSTPya6MSPdSouM6PjTZ6yKgxZkLtX2OMMfmAOeLpPOBQSw0mcqKqqnQaq7jN740mh4LcOqU3Vw/vwoPvruGRuRt5vmAr35vQk2tHdiEUjPF6RDlFfm9UIp8aFdf5sdHjnUP4j9r/xgOPHPF2C+wCvtcSQ4mcDGPM8d9JxEOR0mhWm3h+ftkgvjG6Kw+8uYr73lzJYx9v4vaz+3D+oA4Rs53RSN87cZ0aFdf5sVHTlKvGGWMes9Ze2wrzNLuhQ4fagoICr8eQVlBRUUEoFPJ6DJEGRWqjc9bu5f43V7Fy50EG56Zy5zl9GNk93eux5CREaqMSOdSouM7VRo0xC6y1Q4/1XJPOIfTrYlCiix+P2ZboEqmNjumZwRvfG82vLh/MnkMVXPXwJ1z7yKcs217k9WhygiK1UYkcalRc58dGm3TbCWPMVmoOE/031trOzTqRyEmqrNTFLcRtkdxoTMBw2ZBcpg3qwOPzN/PHmeuY9vuPOH9wDrdN7kXX9CSvR5QmiORGJTKoUXGdHxtt6n0Iv3bU4w7ALcAzzTuOiIj4WXxsDDeMzeMrZ3Xib7M28I+PNvLW0p1ceVYnvj+xJ5kp8V6PKCIiIkdo0jmEx/xAY7KBt621pzXLIMZ8F/g6MBB42lr79SOe6wpsBEqO+JCfW2vvPd7n1TmE0aO8vJz4eP1jU9wVjY3uOVjO7z9Yx9OfbiE2JsD/G92NG8fl0SY+1uvR5BiisVHxFzUqrnO10VM+h7ABFUC3U/j4o+0Afsa/Xs30aG2ttcm1f467GJTo4sdd9BJdorHRzDbx3HvRAGbcOo5J/bL4w4frGPuLD3l49gbKqw57PZ4cJRobFX9Ro+I6Pzba1HMIf3rUmxKBc4G3mmsQa+1LtV9rKJDbXJ9XoocffwAlukRzo13Tk/j9V0/nprF5/OKd1dz35koembuRH0zqxSVndCQYcyq/n5TmEs2Nij+oUXGdHxtt6v8DdzrqTzzwIHBdC83VkM3GmG3GmH8aY3RNcxERnxnQMZXHrj+Lp24YRmabeO54cQlTfzuHt5ft5GRPYRAREZGTd9LnELYUY8zPgNyjziFMBvoAi4A04I9AirX27AY+x43AjQC5ublD1q5dS2VlZf2KPSkpierq6vrLwiYlJREOhykrKwMgMTERgNLSUgASEhIIBAKUlNScwhgKhQgGg/WP4+LiiIuLo7i4uP5xKBSiuLgYay2xsbGEQiFKS0sJh8MEg0Hi4+PrHwcCARITEykvL6e6urr+cUVFBVVVVRhjSE5OpqKion4bkpOTtU1HbZMxhsTExIjapkj8PkXzNu3bt49QKBRR23Qq36eEhATeWrKD383axKb95fTvkMJ3xnTirE7JBAIBX26T379Phw8fJjU1NaK2KRK/T9G8TUVFRcTExETUNkXi9ymat6miooL27ds7t01paWkNnkN43AWhMSZIzVVGJwPpQCEwA3jCWlvV6Ad/+TlmAuMaeHqutXb0Ee/7bwvCY3y+bGAnkGqtPdjY19ZFZaLHoUOHSElJ8XoMkQap0WOrPhzmlUU7+M2MNWw7UMaQLu24bUov3dzeA2pUXKdGxXWuNnrSF5UxxqQC84CfA1XAwtr//i8wr/b547LWjrfWmgb+jD7+Z/j3T1k34kl8rEQoP94IVKKLGj22YEyAy4bk8sFt47nv4gFsP1DGVQ9/wlUPf8yCzfu9Hi+qqFFxnRoV1/mx0eNdVOYBYC+Qb62tv+WDMSYJeK72+e80xyC1eyKDQAwQY4yJB6qttdXGmGHAF8BaoB3wO2CmtbaoOb62iIh4Ly4Y4OphXbj0jFye+mQLf5q5jkv/PJ/83hncOrk3A3Ob9DtIEREROQHHu6jMRcC3j1wMAtQ+vhm4uBln+QlQBvwnNYeoltW+DSAPeBs4BCyj5pYXX23Gry0RICkpyesRRBqlRpsmPjaG60d3Y/Yd+dw5tQ8Lt3zB+X/4iG89voDVuw55PV5EU6PiOjUqrvNjo42eQ2iMKQHaWGv/7WZRtXv0iqy1Tm+1ziGMHiUlJb78IZTooUZPzsHyKh75aCN/n7ORkspqLhicwy0Te5KXkez1aBFHjYrr1Ki4ztVGT+XG9OuBCQ08NxHYcCqDiTSnuis3ibhKjZ6cNvGx/MekXsy5I59vjevOu8t3M/mh2dz+/GK27i/1eryIokbFdWpUXOfHRo+3IHwQeMwYc6kxJgBgjAkYYy4DHq19XkREpMW1S4rjzql9mH1HPteN6Mqri3eQ/6uZ/OilJVoYioiInKSm3HbiNuAeIETNLSfSqTmH76fW2l+29ICnSoeMRo/S0tL6+7qIuEiNNq+dRWX8eeZ6nvl0K2FruXxoLt8Z34NO7fW/8clSo+I6NSquc7XRxg4ZbdKN6Y0xKcBIvrwP4fzj3f/PFVoQRg9XfwBF6qjRlnH0wvCyIbncnK+F4clQo+I6NSquc7XRUzmHEABr7SFr7TvW2idr/+uLxaBEl9JSHTImblOjLaNDagI/vXAAs+4Yz9XDOvPSwu3k/2omd76gQ0lPlBoV16lRcZ0fG23SglBERMR1HVIT+J8LBzD7jny+NrwLLy/6cmG4ZZ///g9aRESkNWhBKBEjISHB6xFEGqVGW0d2ajz3XNCf2bcfsTD89UzueGGxFobHoUbFdWpUXOfHRrUglIgRCChncZsabV11C8M5d+RzzfAuvLJoB/m/nsntzy9m874Sr8dzkhoV16lRcZ0fG/XfxCINKCnRP/DEbWrUG1ltvlwYXjuiC68t3sGEX8/i1ucWsW5PsdfjOUWNiuvUqLjOj41qQSgiIlEhq008/31+zcLwuhFdeXPpTiY/NIubn1zI8h1FXo8nIiLiiaDXA4g0l1Ao5PUIIo1So27IbBPP3ef34zv53Xnko408Nn8z05fuZGKfTG6e0IMzOrfzekTPqFFxnRoV1/mxUe0hlIgRDOr3G+I2NeqW9OQQd0ztw9w7J3Dr5F4s2HKAS/40j6v//jHz1hfSlPv0Rho1Kq5To+I6PzaqBaFEDD8esy3RRY26KTUxlu9P7MncOyfw43P7smZ3MVc9/AmX/WU+H67aE1Vzx+vJAAAgAElEQVQLQzUqrlOj4jo/NqoFoYiICJAUCnLD2Dzm3JHPvRf2Z1dROd949DOm/f4j3lq6k3A4ehaGIiISPbQglIgRFxfn9QgijVKj/hAfG8M1I7ry4Q/H84vLBlFaeZhvP7mQKb+Zzcufb6P6cNjrEVuMGhXXqVFxnR8b1YJQIoYffwAluqhRf4kLBrhiaCdm3DqO3331dGKM4QfPLmbCr2fx5CebKa867PWIzU6NiuvUqLjOj41qQSgRo7hY9xMTt6lRf4oJGC4YnMNbt4zhb9cMoV1iLD9+eRmjf/4hf5q5joPlVV6P2GzUqLhOjYrr/Nio/y6DIyIi4oFAwDClfzaT+2Uxf/0+/jxrPb94ezV/+nA9Vw/rzPWju5HVJt7rMUVERE6IFoQSMfy4i16iixqNDMYYRvZIZ2SPdJZtL+Ivs9bz8JwN/HPuJi45oyM3js0jLyPZ6zFPihoV16lRcZ0fG9WCUCKGH28EKtFFjUaeAR1T+cNVZ7B5XwkPz9nA8wXbeLZgK2f3y+Zb47tzWqe2Xo94QtSouE6Niuv82KjOIZSI4cdjtiW6qNHI1SUtiZ9dNJCP7pzAd8Z3Z976Qi7641yu/Nt8Zq72z70M1ai4To2K6/zYqBaEEjH88g8uiV5qNPJlpIS4/ew+zPvRRH58bl82Fpbw9X9+xrm/+4hXF213/pYValRcp0bFdX5sVAtCiRixsbFejyDSKDUaPZJrb3I/+458fnHpICqqD3PLM4vI//VMHpu/ibJKN29ZoUbFdWpUXOfHRo0fV7EnYujQobagoMDrMaQVlJeXEx+vK/yJu9Ro9AqHLe+t3M1fZq3n8y1f0C4xlq8N78K1I7qSkeLO+SZqVFynRsV1rjZqjFlgrR16rOe0h1AiRmlpqdcjiDRKjUavQMBwdv9sXvr2SJ69cThDurTnDx+uY9TPP+DOF5awdvchr0cE1Ki4T42K6/zYqK4yKhEjHHb73BwRNSrGGIblpTEsL40Ne4v5x0cbeWFBzZVJ83tncMOYPEZ0T8MY48l8alRcp0bFdX5sVHsIJWIEg/r9hrhNjcqR8jKSue/igcz7zwn8YFIvlmwr4qq/f8K033/EK59vp8qDC9CoUXGdGhXX+bFRnUMoEcPVY7ZF6qhRaUx51WFe+Xw7D8/ZwPq9JXRIjecbo7py5VmdaRPfOhcpUKPiOjUqrnO1UZ1DKFHBj8dsS3RRo9KY+NgYrjyrM+/9YByPfH0oXdOSuP/NVYx84APufWMF2w60fD9qVFynRsV1fmzUf/s0RRrgx2O2JbqoUWmKQMAwoU8WE/pksWx7EQ/P2cCj8zbx6LxNnDuwAzeM6cag3LYt8rXVqLhOjYrr/NioFoQSMQIB7fAWt6lROVEDOqby2ytP586pfXh03iae/mQLry/ewZld23H9qG5M7pdFMKb5ulKj4jo1Kq7zY6M6h1AihqvHbIvUUaNyqg6VV/HsZ1v5v/mb2Lq/jI5tE7huZBe+MrQzqYmnfp6hGhXXqVFxnauN6hxCiQrl5eVejyDSKDUqpyolPpZvjslj5g/z+es1Q+jUPoH731zF8Afe579eWcb6vcWn9PnVqLhOjYrr/NioDhmViFFdXe31CCKNUqPSXGJqb3R/dv9slu8o4tG5m3j2s608/vFmxvfO4BujujG2Z/oJ389QjYrr1Ki4zo+Nag+hRAw/HrMt0UWNSkvon5PKLy8fzNza+xku33GQ6x75lMkPzeaJjzdTWtn0f5yoUXGdGhXX+bFRnUMoEcPVY7ZF6qhRaQ0V1YeZvmQn/5y7iaXbi0hNiOXKszpx7YiudGyb0OjHqlFxnRoV17naaGPnEOqQUYkYFRUVTv4AitRRo9IaQsEYLjkjl4tP78iCzQd4ZO5GHp69gb/P2cjU/tlcP7orZ3Rud8zDSdWouE6Niuv82KgWhBIxqqqqvB5BpFFqVFqTMYahXdsztGt7th0o5fH5m3n60y1MX7qTgR1TuXZEF84fnEN8bEz9x6hRcZ0aFdf5sVH/HeQq0oATvXiCSGtTo+KV3HaJ/Ojcvnx810TuvWgA5VWHuf2FJYx44H3+961VbN1fCqhRcZ8aFdf5sVGdQygRo6KiglAo5PUYIg1So+IKay3zN+zjsXmbeXfFLgAm9MniqjNzyO/bwZf/oJHooNdRcZ2rjeocQokKrv4AitRRo+IKYwwju6czsns6278o46lPNvPMp1uZsXI3eRlruHZ4Fy4dkktK/Knf7F6kOel1VFznx0Z1yKhEjMrKSq9HEGmUGhUXdWybwO1n92Hejybw03O70yY+lnteX8Hw+2tudr929yGvRxSpp9dRcZ0fG9UeQhERESEUjOHcfulcO7YPi7d+wWPzN/NsQc3N7kfkpXHdyC5M6ptFMEa/SxYRiSQ6h1Aihqv3fRGpo0bFdUc3ur+kkmc/28oTH29m+xdldEiN52vDu/CVMzuRnuyvQ6IkMuh1VFznaqONnUOoX/NJxPDjLnqJLmpUXHd0o+2T4vj2+O7MviOfv10zhO4ZyfzyndWMfOAD/uOZzynYtJ9I/8WyuEWvo+I6PzaqQ0YlYvjxB1CiixoV1zXUaEzAMKV/NlP6Z7NuTzGPz9/ESwu388qiHfTJTuHqYZ256PSOugiNtDi9jorr/Nio9hCKiIhIk/XITOZ/LhzAx3dN5H8vGUgwxvBfry5n2P3v86OXlrBse5HXI4qIyAnQHkKJGElJSV6PINIoNSquO5FGk0JBrjyrM1ee1ZnFW7/gyU828/Ln23n6060Mzk3l6uFdOH9QDglxMS04sUQbvY6K6/zYqC4qIxHj0KFDpKSkeD2GSIPUqLjuVBstKqvi5YXbeOKTLazbU0xKfJBLz8jla8M70yNT7cup0+uouM7VRnVRGYkKFRUVXo8g0ig1Kq471UZTE2L5+qhuvPeDsTx743Dye2fy5CebmfTgbL7y1/m8tngHFdWHm2laiUZ6HRXX+bFRHTIqIiIizcoYw7C8NIblpVFY3I/nC7bx1Keb+f7Tn5OWFMcVZ3biqrM606l9otejiohEPR0yKhGjrKyMhIQEr8cQaZAaFde1ZKPhsGXOukKe+Hgz76/cjQXG9szgq2d1ZmLfTGJ1w3tpAr2OiutcbbSxQ0ad2ENojAkBfwImAe2BdcBd1tq3jnificAfgc7AJ8DXrbWbPRhXHBUOh70eQaRRalRc15KNBgKGcb0yGNcrg51FZTzz6Vae+WwL33piARkpIS4bksuVZ3aiS5r/LsggrUevo+I6Pzbqyq/jgsBWYByQCvwX8JwxpiuAMSYdeKn27e2BAuBZLwYVd5WVlXk9gkij1Ki4rrUa7ZCawA8m92LunRP4+7VDGZybyl9nrWfcL2fy1b99zKuLtutcQzkmvY6K6/zYqBN7CK21JcA9R7zpDWPMRmAIsAm4BFhurX0ewBhzD1BojOljrV3VutOKiIhIcwjGBJjUL4tJ/bLYVVTO8wVbebZgK7c8s4i2ibFccnouXz2rEz2z3Ltin4hIpHBiQXg0Y0wW0AtYXvum/sDiuuettSXGmPW1b9eCUABITNTFCcRtalRc52Wj2anxfG9iT27O78FH6wp59rOtPP7xJh6Zu5EhXdpx5ZmdmKb7GkY9vY6K6/zYqHMLQmNMLPAk8H9H7P1LBvYe9a5FwDF/ZWiMuRG4ESA3N5fy8nIqKyuprKwEam4YWV1dXX9Z2KSkJMLhcP0u3rpvZGlpKQAJCQkEAgFKSkoACIVCBIPB+sdxcXHExcVRXFxc/zgUClFcXIy1ltjYWEKhEKWlpYTDYYLBIPHx8fWPA4EAiYmJlJeXU11dXf+4oqKCqqoqjDEkJydTUVFRvw3JycnapqO2qfZ7H1HbFInfp2jepv3791NaWhpR2xSJ36do3qa6ub3epjM7JdOvfWduGd2Bd9Yc4MVFu7n9hSXc89pyzhuQyZVndqJziona71M0b9OhQ4coLS2NqG2KxO9TNG9TeXk5xhjntqkxrXKVUWPMTGrODzyWudba0bXvFwCeAtoAF1prq2rf/lsg1lr7nSM+51LgHmvti419bV1lNHoUFhaSnp7u9RgiDVKj4jpXG7XW8unG/Tzz2VbeXLqTiuowAzumcuVZnbhgcA4p8bFejyitxNVGReq42qjnVxm11o4/3vsYYwzwDyALOLduMVhrOXDdEe+bBHTny0NKRUREJEIdeV/De87vz8ufb+OZz7by45eX8bM3VjJtUAeuPKsTZ3RuR80/J0REpKmcuQ+hMeYvwGnAJGtt8VHPZVBzK4rrgenA/wDjrLXDj/d5tYcwepSUlJCUpMuVi7vUqLjOT41aa1m8rYhnPt3Ca4t3UFp5mLyMJC4f0olLz+hIZpt4r0eUFuCnRiU6udpoY3sInVgQGmO6UHM10Qqg+oinbrLWPln7PpOAPwBd+PI+hJuO97m1IIwert4IVKSOGhXX+bXR4opq3lyyk+cXbOWzTQeIqb3n4RVDc5nQJ4u4oCt32ZJT5ddGJXq42qjnh4weT+0N5hs9xsNaOwPo0zoTiR+VlJQ4+QMoUkeNiuv82mhyKMgVZ3biijM7sWFvMS8s2MaLC7fxrSf20D4pjotO68jlQ3Pp26GN16PKKfJroxI9/NioEwtCERERkeaQl5HMHVP7cOvkXsxZV8jzBV/evmJgx1QuH5rLhYM7kpqoC9GIiIAWhBJBQqGQ1yOINEqNiusiqdFgTID83pnk985kf0klry7aznMF27j71eX8bPpKpvTL4oqhnRjVI52YgC5E4xeR1KhEJj82qgWhRIxgUDmL29SouC5SG22fFMc3RnXjG6O6sWx7Ec8XbOWVRTt4Y8lOOqTGc9mQXC4bkkuXNPcuBCH/KlIblcjhx0b9N7FIA/x4zLZEFzUqrouGRgd0TGVAx1R+dG5fZqzczfMF2/jDh+v4/QfrGNatPZcP7cQ5A7JJCumfSC6KhkbF3/zYqF7tREREJOrEx8YwbVAO0wblsLOojJcWbue5gq388PnF3P3qMqYOyObSM3IZkZdGQIeUikgE04JQIkZcXJzXI4g0So2K66K10Q6pCdyc34PvjO/OZ5sO8NLCbUxfspOXFm4nJzWei07vyCVn5NIjM9nrUaNetDYq/uHHRp24D2FL0n0Io0d5eTnx8boRsbhLjYrr1OiXyqsO8+6K3by0cBuz1+wlbGFwp7ZcekZHzh+UQ7sk//2jLxKoUXGdq406fx9CkeZQXFzs5A+gSB01Kq5To1+Kj43hgsE5XDA4hz0Hy3l10Q5eXFhzldJ731jBhD6ZXHpGLuN7Z+rG961IjYrr/NioFoQiIiIijchsE88NY/O4YWwey3cU8dLC7by6aDvvLN9N+6Q4LhicwyVndGRgx1SM0fmGIuIvWhBKxPDjMdsSXdSouE6NHl//nFT656Tyo3P6MHvtXl5cuJ2nPt3Co/M20TMzmUvOyOWi03PokOqvqwz6hRoV1/mxUZ1DKBGjoqLClzcDleihRsV1avTkFJVV1V6EZhsFmw9gDIzqns6lQzpydv9sEuP0+/fmokbFda42qnMIJSoUFxc7+QMoUkeNiuvU6MlJTYjlqmGduWpYZzYVlvDS59t5aeE2fvDsYhLjljGlXxYXnt6RMT3SCcbofMNToUbFdX5sVAtCiRiRvrdb/E+NiuvU6Knrmp7ErZN78R8Te/LZpv28smgH05fs4JVFO0hPjmPaoBwuPC2H0zq11fmGJ0GNiuv82KgWhBIxYmNjvR5BpFFqVFynRptPIGAYlpfGsLw07rmgHzNX7+XVRV+eb9g1LZELT+vIRad3pFt6ktfj+oYaFdf5sVGdQygRw9X7vojUUaPiOjXa8g6WV/H20l28smg78zfsw9be3/Ci03KYNiiHjBR/HWrW2tSouM7VRhs7h1ALQokY+/fvp3379l6PIdIgNSquU6Ota2dRGa8v3sErn+9gxc6DxAQMo3ukc9HpOUzpl01SSAdyHU2NiutcbVQXlZGoEA6HvR5BpFFqVFynRltXh9QEbhzbnRvHdmfN7kO88vl2Xl20gx88u5iE2GVM7pfFxad3ZHTPdGJ1MRpAjYr7/NioFoQSMYJB5SxuU6PiOjXqnV5ZKdwxtQ8/nNKbBVsO8PLn25m+ZCevLd5B+6Q4pg3qwIWndeSMztF9MRo1Kq7zY6M6ZFQihqvHbIvUUaPiOjXqlsrqMLPW7OWVz7czY+VuKqrD5LZL4PzBOVwwOIc+2SlRtzhUo+I6VxvVIaMSFUpLS538ARSpo0bFdWrULXHBAJP7ZTG5XxaHyqt4e9kuXl+yk7/N3sCfZ66nR2Yy5w/K4fzBHcjLSPZ63FahRsV1fmxUC0KJGH48ZluiixoV16lRd6XEx3L50E5cPrQThcUVvLVsF68v3sFDM9bw0Iw1DOjYhvMH5TBtcA4d2yZ4PW6LUaPiOj82qgWhRIxAQCfci9vUqLhOjfpDenKIa4Z34ZrhXdhZVMb0JTt5ffEOHnhrFQ+8tYqhXdpxwWk5nDOgQ8TdxkKNiuv82KjOIZSI4eox2yJ11Ki4To3626bCEt5YsoPXF+9k9e5DBAyM7J7OBYNzOLt/NqmJ/rth9tHUqLjO1UZ1H0ItCKPCF198Qdu2bb0eQ6RBalRcp0Yjx+pdh3h98Q5eX7KDzftKiY0xjOuVwfmDc5jUN8u39zhUo+I6VxvVRWUkKlRXV3s9gkij1Ki4To1Gjt7ZKfTO7s1tU3qxdHsRry3awRtLdjJj5R7iYwNM7JvFBYNzGNcrg/jYGK/HbTI1Kq7zY6NaEErE8OMx2xJd1Ki4To1GHmMMg3LbMii3LXed25eCzQd4bfF23ly6i+lLdpISCjKpXxbnDezAmF7phIJuLw7VqLjOj43qkFGJGK4esy1SR42K69Ro9Kg+HGbe+n28vngH767YTVFZFSmhIJP7ZXGuw4tDNSquc7VRHTIqUaGiosLJH0CROmpUXKdGo0cwJsDYXhmM7ZXBfdVh5q0vZPqSnby7Yjcvfb69fnF43qAOjO7pzuJQjYrr/NioFoQSMaqqqrweQaRRalRcp0ajU1wwwPjemYzvncl91WHmri/kzSMXh/G1i8OB3i8O1ai4zo+NakEoEcMY4/UIIo1So+I6NSpxwQD5vTPJP2px+M7yXby08MvF4bRBHRjdI4O4YOueL6VGxXV+bFTnEErEqKioIBSKrBvwSmRRo+I6NSoNqaxdHE5fspN3l+/iYHk1KfFBpvTL5rxB2a22OFSj4jpXG9U5hBIVXP0BFKmjRsV1alQacuSew8qLBzJ3XSHTl9bsOXxx4TbaxAeZ3C+baYM6MKpHeostDtWouM6PjWpBKBGjsrLS6xFEGqVGxXVqVJoiLhggv08m+X0yub92cfjGkp28u+LLxeGkfllM7Z/N2Ga+z6EaFdf5sVEtCEVERETkpBy5OKysHshH6/YyfckuZqzczUsLt5MYF0N+n0ym9s8mv08mySH901PENfqplIiRnJzs9QgijVKj4jo1KqciLhhgQp8sJvTJoupwmI837OOtZbt4d/kupi/ZSVwwwNieGUwdkM3kvlmkJsae8NdQo+I6PzaqBaFEjMrKSt/d90WiixoV16lRaS6xMQHG9MxgTM8M7r1wAAs2H+CtZTt5Z1nN3sNgwDCiexpTB2QzpV82GSlNO+dKjYrr/NioFoQSMfx4zLZEFzUqrlOj0hJiAoazurXnrG7tuXtaP5ZsK+KtZbt4e9lOfvzyMn7yyjLO7Nqeqf2zmTogm5y2CQ1+LjUqrvNjo1oQioiIiEirMMYwuFNbBndqy51Te7N69yHeWrqLt5ft4qdvrOCnb6xgcKe2TO2fzTkDsumanuT1yCIRT/chlIhRVlZGQkLDv1UU8ZoaFdepUfHShr3FvL28ZnG4ZFsRAH2yU5g6IJtzBnSgV1Yy5eXlalSc5urraGP3IdSCUCLGoUOHSElJ8XoMkQapUXGdGhVXbDtQyjvLd/P2sp0UbD6AtdAtPYnxPdsx7bTOnN6pLYGA8XpMkX/j6uuoFoRaEEaFwsJC0tPTvR5DpEFqVFynRsVFew6W8+6K3byzfBfz1u/jcNiSnhxicr8spvTPYmT3NELB5rvXocipcPV1tLEFoc4hFBERERFnZbaJ52vDu/C14V3YuG0XSwoP8+7y3by2aDtPf7qF5FCQcb0zmNIvi/w+mbSJP/HbWYhEMy0IJWIkJenEc3GbGhXXqVFxXXZaKt1yE7jwtI6UVx1m/vp9vLtiF++t2M30JTuJjTEMz0tjSv+aex1mp/rr8v/if358HdUhoxIxSkpKfPlDKNFDjYrr1Ki4rqFGD4cti7Ye4N3lNYeWbtpXCsDgTm2Z0i+Ls/tn0SPTvfO6JPK4+jqqcwi1IIwKrh6zLVJHjYrr1Ki4rimNWmtZt6eYd1fs5t3lu1hce8XSvIwkpvTLZkr/LE7L1UVppGW4+jqqcwhFREREJCoYY+iZlULPrBRuzu/BzqIyZqzYzbsrdvP3ORv4y6z1ZKTUXpSmXxYjdFEaiXLaQygRo7S0lMTERK/HEGmQGhXXqVFx3ak2WlRWxczVe3h3+W5mrt5DSeXhmovS9MpgUr9MxvfKpF1SXDNOLNHG1ddR7SEUERERkaiXmhDLhad1/JeL0ryzfBfvr9rD9KU7CRgY2qU9k/plMrFvFt0zkr0eWaTFaUEoEcPV38iI1FGj4jo1Kq5rzkbjY2PI75NJfp9MwmHL0u1FzFi5mxkr93D/m6u4/81V5KUnMbFvJpP6ZjGkSzuCMYFm+doSufz4OqoFoYiIiIhEtUDAMLhTWwZ3asttU3qz7UApH6zaw3srdvPovE08PGcjbRNjye+dycS+mYztlaH7HUrE0IJQIkZCQoLXI4g0So2K69SouK61Gs1tl8i1I7py7YiuHCqvYs7aQmas3M2Hq/bw8ufbiY0xDOuWxqS+NYeWdmrvrz1C0nL8+Dqqi8pIxCgrK/PlD6FEDzUqrlOj4jqvGz0ctizccqDm0NIVu1m/twSAPtkpTKxdHOqWFtHN60Yb4vx9CI0xIeBPwCSgPbAOuMta+1bt812BjUDJER/2c2vtvcf73FoQRg9X7/siUkeNiuvUqLjOtUY3Fpbw/srdzFi5m882HeBw2JKeHGJCnwwm9c1idM90EuN0QF40ca3ROn64ymgQ2AqMA7YA5wLPGWMGWms3HfF+ba211R7MJyIiIiLyL7qlJ/HNMXl8c0weX5RWMmvNXt5bsZu3lu7iuYJtxAUDjOyexoQ+meT3ztShpeIkJxaE1toS4J4j3vSGMWYjMATY5MVM4j+hUMjrEUQapUbFdWpUXOdyo20T4+pvaVFZHeazTfuZsXI3H6zaw92vLgeW0ysrmfzeNVc2HdKlHbG6amnEcbnRhjhxyOjRjDFZwGbgNGvtqiMOGd0BWOA94HZrbeHxPpcOGY0erh6zLVJHjYrr1Ki4zo+NWmvZUFjCh6v28MGqPXy6cT/VYUtKfJCxvTKY0DuT8b0zSEv230JC/p2rjTp/DuGRjDGxwFvAemvtTbVvSwb6AIuANOCPQIq19uwGPseNwI0Aubm5Q9auXUtlZSWVlZUAJCUlUV1dTUVFRf3jcDhMWVkZQP29Q0pLS4GaqwUFAgFKSmpOYQyFQgSDwfrHcXFxxMXFUVxcXP84FApRXFyMtZbY2FhCoRClpaWEw2GCwSDx8fH1jwOBAImJiZSXl1NdXV3/uKKigqqqKowxJCcnU1FRUb8NycnJ2qajtqmsrIz09PSI2qZI/D5F8zZt376d1NTUiNqmSPw+RfM2FRcXk52dHVHbFInfp2jepl27dpGcnOzrbSosKmbe+v3M3fgF8zYWsbe4EgP075DMhD6ZjOyWSvd2sfXb6Idtiob2mrpNRUVF5OTkOLdNaWlp3i4IjTEzqTk/8FjmWmtH175fAHgKaANcaK2tauDzZQM7gVRr7cHGvrb2EEYPV0/iFamjRsV1alRcF2mNhsOW5TsO8sGqPXyweg9Ltn2BtZCZEqo/tHR0z3SSQ06c5SVN4Gqjnl9Uxlo7/njvY4wxwD+ALODchhaDdZ+y7sNOfTqJFHFxcV6PINIoNSquU6PiukhrNBAwDMxNZWBuKrdM6klhcQUzV+/lw1V7eHPpTp4t2Fp/z8P8PplM6JNJt/Qkr8eWRvixUWcOGTXG/AU4DZhkrS0+6rlhwBfAWqAdNbeoyLTW5h/v82oPYfQoLy8nPj7e6zFEGqRGxXVqVFwXTY1WHQ5TsOkAH66uOfdw3Z6afx53S08iv3fN4vCsbu2JC+rCNC5xtVHP9xAejzGmC3ATUAHsqtlZCMBN1tongTzgfiATOEjNRWW+6sGo4rDi4mInfwBF6qhRcZ0aFddFU6OxMQFGdE9jRPc07jq3L1v3l9YcWrpqD098splH5m4kKS6GUT3SGdc7g/G9M+nY1r2LmUQbPzbqxILQWruZRg7/tNY+DTzdehOJiIiIiLijU/tErhvZletGdqW0spp56/bxweo9zFq9l3dX7AagZ2Yy43rVLA7P7NaOUDDG46nFD5xYEIo0Bz8esy3RRY2K69SouE6N1kiMCzKpXxaT+mVhrWX93mJmrt7LrDV7eWz+Zv7+0UYSYmMY2T2tZu9hr0w6pyV6PXZU8GOjWhBKxPDjjUAluqhRcZ0aFdep0X9njKFHZgo9MlP45pg8Siur+XjDPmau3svM1Xt5f9UeYDnd0pMY1yuDcb0zGJGXRnys9h62BD82qgWhRIzi4mJf/hBK9FCj4jo1Kq5To8eXGBdkQp8sJvSp2Xu4aV8pM1fvYdaavTz96RYenbeJUDDA8Ly02sNLM+iWnsQR1/CQU+DHRrUglIjhyhVzRRqiRsV1alRcp0ZPjDGGbulJdEvvxjdGdaO86jAfb9jHrDV7mbV6Lz99YwU/fQM6tU9gfK9MxvfOYET3NEAjb/wAABJ0SURBVBLjtEQ4WX5sVN9tiRixsbFejyDSKDUqrlOj4jo1emriY2MY3zuT8b0z4XzYsq+UWWtq9h6+sGAbj3+8mbiYAGd1a1+/97BHZrL2Hp4APzbqzH0IW4ruQxg9XL3vi0gdNSquU6PiOjXaciqqD/PZxgPMWrOHmav3srb2vocd2yYwpmc6Y3tlMKp7OqmJ/lvwtCZXG3X+PoQizaG0tNTJH0CROmpUXKdGxXVqtOWEgjGM7pnO6J7p/Pg82P5FGbNW72XWmj1MX7KTZz7bSsDAoNy2jK1dIA7u1JbYmIDXozvFj41qQSgRIxwOez2CSKPUqLhOjYrr1Gjr6dg2gauGdeaqYZ2pPhxm0dYvmL22kDlr9/KHD9fxuw/WkRIKMqJ7GmN6ZTCuZ4ZubYE/G9WCUCJGMKicxW1qVFynRsV1atQbwZgAQ7u2Z2jX9tw6uRdFpVXMW1/I7LWFzF6zl3dX7AagS1oiY3qmM6ZnBiO7p5ESH32Hl/qxUf9NLNIAv+2el+ijRsV1alRcp0bdkJoYyzkDO3DOwA5Ya9lYWMKc2r2HLy3czhMfbyEmYDijc1vG9MxgTM90BuW2JSYQ+Ren8WOjWhBKxPDjMdsSXdSouE6NiuvUqHuMMeRlJJOXkcx1I7tSWR1m4ZYDzFm7lzlrC3loxhoefG8NqQmxjO6RXrMHsVcGHdsmeD16i/Bjo1oQSsTw4zHbEl3UqLhOjYrr1Kj74mpvej88L43bz4b9JZXMXVdzaOmctYVMX7oTgO4ZSYzpmcHYXukMz4ucex/6sdHI+F9eBAgEdJUrcZsaFdepUXGdGvWf9klxnD84h/MH52CtZd2e4vqL0zzz2RYenbeJ2BjDkC7tGN0jnVE90hnYMZWgT69e6sdGdR9CiRiu3vdFpI4aFdepUXGdGo0s5VWHWbD5ALPX7OWjdYUs33EQgJT4ICPy0hjds2aBmJeehDH+OP/Q1UZ1H0KJCq7+AIrUUaPiOjUqrlOjkSU+NoZRtXsFAfYVVzB/wz7mritkztrC+quXdkiNZ1SPdEb3SGdkjzQyU9xtwI+NakEoEaO6utrrEUQapUbFdWpUXKdGI1tacohpg3KYNigHgC37SvloXSFz1xUyY+VuXliwDYDeWSk1C8SeaZzVLY3kkDtLGj826s7/eiKnyI/HbEt0UaPiOjUqrlOj0aVzWiJXpXXmqmGdCYctK3YerF8gPvnJZh6Zu5FgwHB657b1exAHd2pLrIfnH/qxUZ1DKBHDj7voJbqoUXGdGhXXqVGpU151mIWbD9QvEJdsL8JaSIqLYXheWu0exHR6Zia36vmHrjaqcwglKlRUVDj5AyhSR42K69SouE6NSp342BhG9khnZO35h1+UVvLxhn21C8R9vL9qDwAZKaH6q5eO6pFGh9SWvf+hHxvVglAiRlVVldcjiDRKjYrr1Ki4To1KQ9omxjF1QAemDugAwLYDpcxbV7NAnL1mLy9/vh2AvIwkRnVPZ2T3mnsltkuKa9Y5/NioFoQSMfxyOWKJXmpUXKdGxXVqVJoqt10iV5yZyBVndiIctqzefYi56wr5aF0hLy3cxuMfbwagX4c2jOyexsgeaZzZtT0p8bGn9HX92KjOIZSIUVFRQSgU8noMkQapUXGdGhXXqVFpDlWHwyzZVsT89YXMW7+Pgs0HqKwOExMwDOyYWrNA7J7OkC7tSIiLOaHP7WqjjZ1DqAWhRIyDBw/Spk0br8cQaZAaFdepUXGdGpWWUF51mIVbDjB//T7mrd/H4q1fUB22xMUEOL1zW0Z2r7n/4eDctsQFG7+KqKuN6qIyEhUqKyu9HkGkUWpUXKdGxXVqVFpCfGxMzaKvezq3ASUV1Xy2aX/9AvE376/hoRmQEBvDmd3aM7J7GiPy0hjQMZWYwL8eIurHRrUgFBERERERqZUUCjK+dybje2cCUFRaxccb99UuEAv537dWAZASH2RYt7T6cxB7ZaZ4OfZJ04JQIkZycrLXI4g0So2K69SouE6NihdSE2M5u382Z/fPBmDPoXI+3rC//hzEGSt3A5CWFMdZXdty13mJdGqf6OXIJ0QLQokYlZWVvrvvi0QXNSquU6PiOjUqLshMieeCwTlcMDgHqLnFxfz1+5i/YR/z1hWSFPLXEstf04o0wo/HbEt0UaPiOjUqrlOj4qLcdolcPjSRy4d2Yu/evbRv5nsbtrTGL5MjIiIiIiIiTeLH+xBqQSgRIykpyesRRBqlRsV1alRcp0bFdX5sVAtCiRjV1dVejyDSKDUqrlOj4jo1Kq7zY6NaEErEqKio8HoEkUapUXGdGhXXqVFxnR8b1YJQREREREQkSmlBKBHDj8dsS3RRo+I6NSquU6PiOj82qgWhRIxwOOz1CCKNUqPiOjUqrlOj4jo/NqoFoUSMsrIyr0cQaZQaFdepUXGdGhXX+bFRLQhFRERERESilBaEEjESExO9HkGkUWpUXKdGxXVqVFznx0a1IBQREREREYlSWhBKxCgtLfV6BJFGqVFxnRoV16lRcZ0fG9WCUEREREREJEppQSgRIyEhwesRRBqlRsV1alRcp0bFdX5sVAtCiRiBgHIWt6lRcZ0aFdepUXGdHxv138QiDSgpKfF6BJFGqVFxnRoV16lRcZ0fG9WCUEREREREJEppQSgRIxQKeT2CSKPUqLhOjYrr1Ki4zo+NakEoESMYDHo9gkij1Ki4To2K69SouM6PjWpBKBHDj8dsS3RRo+I6NSquU6PiOj82qgWhiIiIiIhIlNKCUCJGXFyc1yOINEqNiuvUqLhOjYrr/NioFoQSMfz4AyjRRY2K69SouE6Niuv82Kix1no9Q4syxuwFNns9h7SKdKDQ6yFEGqFGxXVqVFynRsV1rjbaxVqbcawnIn5BKNHDGFNgrR3q9RwiDVGj4jo1Kq5To+I6PzaqQ0ZFRERERESilBaEIiIiIiIiUUoLQokkf/N6AJHjUKPiOjUqrlOj4jrfNapzCEVERERERKKU9hCKiIiIiIhEKS0IRUREREREopQWhOIrxpiQMeYfxpjNxphDxpjPjTHnHPH8RGPMKmNMqTHmQ2NMFy/nlehmjOlpjCk3xjxxxNvUqDjBGHOlMWalMabEGLPeGDOm9u1qVJxgjOlqjHnTGHPAGLPLGPMHY0yw9jl1Kq3KGPNdY0yBMabCGPPoUc812KOp8XNjzL7aP78wxphW34BGaEEofhP8/+3de7BVZRnH8e9PvIDcFXUEmZxEi7zBlKKNpQ3ejRmSdCgsdVIJMkNltItR4qVMJ7PMC0VCDTme6WJ5LRsv/6hjqcMtLRUaOCIECZyDHi/I0x/ve5rldp8LCXuvzf59Zvactd53nbWfveeZs8+z3/ddC1gJHAsMBr4NtOQPjWHA73LbHsDfgLvqFagZ8FPgr507zlErC0knANcB5wIDgU8Cy5yjVjK3AP8G9gXGkD77pztPrU5WAVcDvyg29iIfLwAmAocDhwGfBqbWIN5e80VlrOFJWgRcCewJnBMRH8/t/YF1wNiIeL6OIVoTkjQZOB34OzAqIs6SdAHOUSsBSY8DcyNibkW7c9RKQ9JzwKURcX/evx4YBDyN89TqRNLVwH4RcU7e7/bvZv57Oy8i5uT+LwHnR8RRdXkBVXiE0BqapH2Ag4ClwMHAws6+iHgNeCm3m9WMpEHAbODSii7nqNWdpD7Ax4C9JL0oqTVPxeuHc9TK5SZgsqTdJY0ATgEexHlq5dJTPr6rP2+XKlddEFrDkrQLsACYn78RHABsrDhsI2k6lFktXUUafVlZ0e4ctTLYB9gF+CzwCdJUvLHAFThHrVweI/3j3Aa0kqbi3Y3z1Mqlp3ys7N8IDCjTOkIXhNaQJO0E/Ap4C7gwN28iTSUpGgS01zA0a3KSxgDHAzdW6XaOWhl05J8/iYhXImId8EPgVJyjVhL5c/5PpLVZ/YFhwFDS2lfnqZVJT/lY2T8I2BQlWrfngtAaTv5GZS7pW+5JEfF27lpKWrDbeVx/4IDcblYrxwH7AyskrQZmApMkPYNz1EogItaTRluq/TPiHLWy2AMYCdwcEW9GxH+AO0hfXDhPrUx6ysd39eftUuWqC0JrRLcCo4EJEdFRaP89cIikSZL6ArOARV5gbjU2h/RBMCY/bgPuA07COWrlcQfwVUl7SxoKzADuxTlqJZFHrpcD0yTtLGkIcDZp/ZXz1Gou52FfoA/QR1LffBuUnvLxl8AlkkZIGk66vsC8OryELrkgtIaS7+sylfSP9mpJm/JjSkSsBSYB1wDrgXHA5PpFa80oIl6PiNWdD9JUkTciYq1z1ErkKtItUf4JPAc8C1zjHLWSOR04GVgLvAhsBi52nlqdXEGacv914Ky8fUUv8vF24B5gMbCE9CXx7bULu2e+7YSZmZmZmVmT8gihmZmZmZlZk3JBaGZmZmZm1qRcEJqZmZmZmTUpF4RmZmZmZmZNygWhmZmZmZlZk3JBaGZmZmZm1qRcEJqZme2AJJ0o6e7CfkgatQ3Oe5ikx9/veczMrBxcEJqZWd1J2lR4bJHUUdifUqMYjpPU2sMx8yS9leN6VdJDkj5ci/j+D9cC39/WJ42IRcAGSRO29bnNzKz2XBCamVndRcSAzgewAphQaFvQm3NI2nn7Rvk/P8hxjgBeBubW6Hl7TdIRwOCIeHI7PcUCYOp2OreZmdWQC0IzMystSUdKekLSBkmvSLpZ0q6F/pD0FUkvAC/ktsvysasknVecKilpN0k3SFohaY2k2yT1k9QfeAAYXhiZHN5dbBHRAbQAYwrxnCbpWUltklZK+m6hb/8cy9n5+ddJ+lahv5+k+ZLWS3ouv47WQv9wSb+VtFbSckkXdRPeKcBj3byvx+T4PlV4H6dLekFSu6SrJB2Q3/s2SS3F9x14FBgvabfu3iMzMys/F4RmZlZm7wAXA8OAo4HxwPSKYyYC44CPSDoZuAQ4HhgFHFtx7HXAQaQibhRplG9WRLxGKqJWFUYmV3UXWC4iPwe8WGh+DfgiMAQ4DZgmaWLFrx4DfCi/llmSRuf27wD7Ax8ETgDOKjzXTsA9wMIc83hghqSTugjvUOAfXcR9EnAnMCkiHil0nQx8FDgKuAyYA0wBRgKH5NcKQES8DLydX4eZmTUwF4RmZlZaEfF0RDwZEZsj4l/A7by3yPteRLyaR+zOBO6IiKUR8TpwZedBkgScD1ycj28nrbObvJVhzZS0AWgnFXdfKMT7aEQsjogtea3dnVXivTIiOiJiIanAOzy3nwlcGxHrI6IV+HHhd44A9oqI2RHxVkQsA37WTexDcnyVziAVeqdGxFMVfddFRFtELAWWAH+OiGURsZE0ejq24vj2/DxmZtbAXBCamVlpSTpI0r2SVktqIxVwwyoOW1nYHl6xX9zeC9gdeDpPQd0APJjbt8YNETGENJrXQWGUTNI4SY/kaZ0bgS9XiXd1Yft1YEAvYv8AaTrrhkLs3wT26SLG9cDAKu0zgJaIWFylb01hu6PK/oB3H85AYEMXz29mZg3CBaGZmZXZrcDzwIERMYhUBKnimChsvwLsV9gfWdheRypsDo6IIfkxOF8gpvI8PYqIFcDXgJsk9cvNvwb+CIyMiMHAbVXi7Up3sa8ElhfiHhIRAyPi1C7OtYg0NbbSGcBESTN6GVNVeX3lrnQxLdXMzBqHC0IzMyuzgUAbsCnf3mFaD8e3AOdKGi1pd2BWZ0dEbCFNs7xR0t4AkkYU1uGtAfaUNLi3wUXEQ8Aq4IJCvK9GxBuSjgQ+39tz5di/IWmopBHAhYW+p4A2SZfni8/0kXRIvppoNffz3qmq5FjHAxdJqlyLuTWOAx6OiDffxznMzKwEXBCamVmZzSQVVe2kYu6u7g6OiAdIa+8eIV3s5Ync1Vm4XJ7bn8xTUP9CnvIZEc+T1vwty9Myu73KaMH1wGX5ipvTgdmS2knFaEsvzwEwG2gFlue4ftMZd0S8A0wgXQxnOWm08+dA1eI1Ip4BNkoaV6VvBakovFzSeVsRX9EU0uinmZk1OEVs1QwZMzOzhpGv4LkE2C0iNtc7nq0haRowOSKqjfT15vdPBKZHROVVTt9vXIcCcyLi6G15XjMzqw8XhGZmtkOR9BngPqA/MB/Ysq2Lou1B0r6kW048ARxIeg03R8SP6hqYmZnt0Dxl1MzMdjRTgbXAS6T7GPa07rAsdiXdVqMdeBj4A3BLXSMyM7MdnkcIzczMzMzMmpRHCM3MzMzMzJqUC0IzMzMzM7Mm5YLQzMzMzMysSbkgNDMzMzMza1IuCM3MzMzMzJqUC0IzMzMzM7Mm9V/AFibuEgxJFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(target_range / 1.0e3, 10.0 * log10(output_snr), '')\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Output Signal to Noise Ratio', size=14)\n",
    "\n",
    "plt.xlabel('Target Range (km)', size=12)\n",
    "\n",
    "plt.ylabel('Output Signal to Noise Ratio (dB)', size=12)\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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